Data Camp Visualization

Background and Overview

DataCamp offer interactive courses related to R Programming. While some is review, it is helpful to see other perspectives on material. As well, DataCamp has some interesting materials on packages that I want to learn better (ggplot2, dplyr, ggvis, etc.). A main document DataCamp_Insights_v001 was written to summarize key insights from:

• R Programming (Introduction to R, Intermediate R, Writing Functions in R)
  
• Importing and Cleaning Data (Cleaning Data in R, Importing Data in to R)
  
• Data Manipulation (dplyr, data.table, xts/zoo)
  
• Data Visualization (base, ggplot2 parts I/II/III, ggvis, geospatial)

The original DataCamp_Insights_v001 document has now been split. The Visualization components are mainly finished and have been moved to this standa-lone DataCamp_Visualization_v002 document. The aspiration is to minimize time spent re-running moderately complex (and thus time/space-consuming) graphing code.

A separate DataCamp_Insights_v002 document continues to contain sections on R Programming, Importing and Cleaning Data, and Data Manipulation.

Data Visualization (base, ggplot2 parts I/II/III, ggvis, geospatial)

Data Visualization (Base)

Graphical tools help to visualize a dataset:

• Exploratory - help us see what is in the data (keep all potentially relevant details)
  
• Exlanatory - help others see what we have found (make editorial decisions for emphasis and clarity)

There are four graphics systems in R:

• Base - easiest to learn (focus of this course)
  
• Grid - powerful set of modules for building other tools (more commonly used by package developers)
  
• Lattice - general purpose, based on grid
  
• ggplot2 - grammar of graphics in R, based on grid

Preview of more and less useful techniques:

• scatterplots can be generated using plot()
  
• sunflowerplots can be generated using sunflowerplot()
  
• boxplots can be created with boxplot()
  
• mosaic plots can be created with mosaicplot()

Quality of graph types - In general, pie charts are not as good as bar charts.

Example code includes:

# Load MASS package
library(MASS)
library(RColorBrewer)  # Need this non-cached for later

# Plot whiteside data
plot(whiteside)

# Plot Gas vs. Temp
plot(y=whiteside$Gas, x=whiteside$Temp, xlab="Outside temperature", ylab="Heating gas consumption")

# Apply the plot() function to Insul
plot(whiteside$Insul)

# Plot Max.Price vs. Price as red triangles
plot(x=Cars93$Price, y=Cars93$Max.Price, pch=17, col="red")

# Add Min.Price vs. Price as blue circles
points(x=Cars93$Price, y=Cars93$Min.Price, pch=16, col="blue")

# Add an equality reference line with abline()
abline(a = 0, b = 1, lty = 2)

# Load the robustbase package
library(robustbase)

## Warning: package 'robustbase' was built under R version 3.2.5

# Set up the side-by-side plot array
par(mfrow = c(1, 2))

# First plot: brain vs. body in its original form
plot(x=Animals2$body, y=Animals2$brain)

# Add the first title
title("Original representation")

# Second plot: log-log plot of brain vs. body
# First plot: brain vs. body in its original form
plot(x=Animals2$body, y=Animals2$brain, log="xy")

# Add the second title
title("Log-log plot")

par(mfrow = c(1, 1))


# Load the insuranceData package
library(insuranceData)

## Warning: package 'insuranceData' was built under R version 3.2.5

# Use the data() function to get the dataCar data frame
data(dataCar)

# Set up a side-by-side plot array
par(mfrow=c(1, 2))

# Create a table of veh_body record counts and sort
tbl <- sort(table(dataCar$veh_body),
            decreasing = TRUE)

# Create the pie chart and give it a title
pie(tbl)
title("Pie chart")

# Create the barplot with perpendicular, half-sized labels
barplot(tbl, las = 2, cex.names = 0.5)

# Add a title
title("Bar chart")

par(mfrow = c(1, 1))


# Unload the MASS package so that dplyr works!
detach(package:MASS)

Charcaterizing a single variable

• What do we expect to see (often, but not always, Gaussian)?
  
• Histogram - best known tool for exploring a single variable
  
• Density Plot - basically, a normalized histogram
  
• Normal Q-Q plot – Gaussian data should be very close to the y=x line

Relationships between two variables

• Scatterplots are generated with plot() – can use plot(y ~ x, data=) as a shortcut also
  
• Sunflowerplots through sunflowerplot() are appropriate for continuous x/y - one petal per occurrence
  
• Boxplots through boxplot() are appropriate for continuous y with factor/categorical x
  
• Mosaic plots through mosaicplot() are appropriate for factor/categorical on both x and y

Complex relationships between variables

• Bagplots - extended 2-D boxplots; inner box has 50%, outer bag has all non-outliers
  
• Correlation coefficients for tendency to move together – can use corrplot::corrplot(cor(, method=“ellipse”)) for correlations
  
• Decision tree models through rpart::rpart()

Example code includes:

data(Cars93, package="MASS")

# Set up a side-by-side plot array
par(mfrow=c(1, 2))

# Create a histogram of counts with hist()
hist(Cars93$Horsepower, main="hist() plot")

# Create a normalized histogram with truehist()
MASS::truehist(Cars93$Horsepower, main="truehist() plot")

par(mfrow=c(1, 1))


# Create index16, pointing to 16-week chicks
index16 <- which(ChickWeight$Time == 16)

# Get the 16-week chick weights
weights <- ChickWeight$weight[index16]

# Plot the normalized histogram
MASS::truehist(weights)

# Add the density curve to the histogram
lines(density(weights))


# Create index16, pointing to 16-week chicks
index16 <- which(ChickWeight$Time == 16)

# Get the 16-week chick weights
weights <- ChickWeight$weight[index16]

# Show the normal QQ-plot of the chick weights
car::qqPlot(weights)

# Show the normal QQ-plot of the Boston$tax data
data(Boston, package="MASS")
car::qqPlot(Boston$tax)

# Set up a side-by-side plot array
par(mfrow=c(1, 2))

# Create the standard scatterplot
plot(rad ~ zn, data=Boston)

# Add the title
title("Standard scatterplot")

# Create the sunflowerplot
sunflowerplot(rad ~ zn, data=Boston)

# Add the title
title("Sunflower plot")

par(mfrow=c(1, 1))


# Create a variable-width boxplot with log y-axis & horizontal labels
boxplot(crim ~ rad, data=Boston, varwidth=TRUE, log="y", las=1)

# Add a title
title("Crime rate vs. radial highway index")

# Create a mosaic plot using the formula interface
mosaicplot(carb ~ cyl, data=mtcars)

# Create a side-by-side boxplot summary
boxplot(Cars93$Min.Price, Cars93$Max.Price)

# Create a bagplot for the same two variables
aplpack::bagplot(Cars93$Min.Price, Cars93$Max.Price, cex=1.2)

# Add an equality reference line
abline(a=0, b=1, lty=2)

# Extract the numerical variables from UScereal
data(UScereal, package="MASS")
numericalVars <- UScereal[, 2:10]

# Compute the correlation matrix for these variables
corrMat <- cor(numericalVars)

# Generate the correlation ellipse plot
corrplot::corrplot(corrMat, method="ellipse")

# Fit an rpart model to predict medv from all other Boston variables
tree_model <- rpart::rpart(medv ~ ., data=Boston)

# Plot the structure of this decision tree model
plot(tree_model)

# Add labels to this plot
text(tree_model, cex=0.7)

Options can be specified either globally (e.g., par(mfrow)) or locally (e.g., cex) or both (cex.main can be global or local):

• More or less, the par() is changing the defaults for all future graphs
  
• The type=“o” provides points overlaid on lines, and is always called locally
  
• The type =“n” means do not plot; is useful for setting up axes and titles and the like for planned future calls to lines() or points() or the like
  
• Can also set things like xlim = range(x) even when just plotting subsets of x

Adding lines and points to plots can help highlight key details (e.g,, outliers in a different color and shape):

• True histograms (MASS::truehist()) can be combined with lines(density()) to overlay the density on the normalized histogram
  
• The abline(a=, b=) means that abline(a=0, b=1) will be an identity line

Adding text to plots helps to explain the findings for the reader:

• Axis labels, titles, legends, text in plot itself
  
• Defaults are sometimes created, but these can often be overridden
  
• Text can be added to the plot with text(x, y, labels, adj), with text typically centered (adj=0.5) around x but can be left-justified (adj=0) to start at x or right-justified (adj=1) to end at x
  • The srt= option cab be added to specify an angle for the text
    
  • The font= options are mosyly for adding bold, italics, or bold/italics to a baseline font

Adding or modifying other plot details:

• The legend() command allows for adding a legend to the graph – x (and optionally y) are the position, with col, legend, pch, cex, etc. all available as options
  
• Custom axes can be created by generating a plot with option axes=FALSE, then using axis(side=) to add the axes – where side is 1 below, 2 left, 3 above, 4 right
  • The at= option is for tick marks, with labels= being for the labels at the tick marks
    
  • The las=2 makes the labels perpindicular, while the las=1 make the labels horizontal (???)
    
• The supsmu() function will put a best-fit smooth together

Example code includes:

# Assign the return value from the par() function to plot_pars
plot_pars <- par()

# Display the names of the par() function's list elements
names(plot_pars)

##  [1] "xlog"      "ylog"      "adj"       "ann"       "ask"      
##  [6] "bg"        "bty"       "cex"       "cex.axis"  "cex.lab"  
## [11] "cex.main"  "cex.sub"   "cin"       "col"       "col.axis" 
## [16] "col.lab"   "col.main"  "col.sub"   "cra"       "crt"      
## [21] "csi"       "cxy"       "din"       "err"       "family"   
## [26] "fg"        "fig"       "fin"       "font"      "font.axis"
## [31] "font.lab"  "font.main" "font.sub"  "lab"       "las"      
## [36] "lend"      "lheight"   "ljoin"     "lmitre"    "lty"      
## [41] "lwd"       "mai"       "mar"       "mex"       "mfcol"    
## [46] "mfg"       "mfrow"     "mgp"       "mkh"       "new"      
## [51] "oma"       "omd"       "omi"       "page"      "pch"      
## [56] "pin"       "plt"       "ps"        "pty"       "smo"      
## [61] "srt"       "tck"       "tcl"       "usr"       "xaxp"     
## [66] "xaxs"      "xaxt"      "xpd"       "yaxp"      "yaxs"     
## [71] "yaxt"      "ylbias"

# Display the number of par() function list elements
length(plot_pars)

## [1] 72

# Set up a 2-by-2 plot array
par(mfrow=c(2, 2))

# Plot the Animals2 brain weight data as points
plot(Animals2$brain, type="p")

# Add the title
title("points")

# Plot the brain weights with lines
plot(Animals2$brain, type="l")

# Add the title
title("lines")

# Plot the brain weights as lines overlaid with points
plot(Animals2$brain, type="o")

# Add the title
title("overlaid")

# Plot the brain weights as steps
plot(Animals2$brain, type="s")

# Add the title
title("steps")

par(mfrow=c(1, 1))


# Compute max_hp
data(Cars93, package="MASS")
max_hp <- max(Cars93$Horsepower, mtcars$hp)

# Compute max_mpg
max_mpg <- max(Cars93$MPG.city, Cars93$MPG.highway, mtcars$mpg)

# Create plot with type = "n"               
plot(mtcars$hp, mtcars$mpg,
     type = "n", xlim = c(0, max_hp),
     ylim = c(0, max_mpg), xlab = "Horsepower",
     ylab = "Miles per gallon"
     )

# Add open circles to plot
points(x=mtcars$hp, y=mtcars$mpg, pch = 1)

# Add solid squares to plot
points(x=Cars93$Horsepower, y=Cars93$MPG.city, pch = 15)

# Add open triangles to plot
points(x=Cars93$Horsepower, y=Cars93$MPG.highway, pch = 6)

# Create the numerical vector x
x <- seq(0, 10, length = 200)

# Compute the Gaussian density for x with mean 2 and standard deviation 0.2
gauss1 <- dnorm(x, mean = 2, sd = 0.2)

# Compute the Gaussian density with mean 4 and standard deviation 0.5
gauss2 <- dnorm(x, mean = 4, sd = 0.5)

# Plot the first Gaussian density
plot(y=gauss1, x=x, type="l", ylab="Gaussian probability density")

# Add lines for the second Gaussian density
lines(y=gauss2, x=x, lty=2, lwd=3)

# Create an empty plot using type = "n"
plot(x=mtcars$hp, y=mtcars$mpg, type="n", xlab="Horsepower", ylab="Gas mileage")

# Add points with shapes determined by cylinder number
points(x=mtcars$hp, y=mtcars$mpg, pch = as.numeric(mtcars$cyl))

# Create a second empty plot
plot(x=mtcars$hp, y=mtcars$mpg, type="n", xlab="Horsepower", ylab="Gas mileage")

# Add points with shapes as cylinder characters
points(x=mtcars$hp, y=mtcars$mpg, pch = as.character(mtcars$cyl))

# Build a linear regression model for the whiteside data
data(whiteside, package="MASS")
linear_model <- lm(Gas ~ Temp, data=whiteside)

# Create a Gas vs. Temp scatterplot from the whiteside data
plot(y=whiteside$Gas, x=whiteside$Temp)

# Use abline() to add the linear regression line
abline(linear_model, lty=2)

# Create MPG.city vs. Horsepower plot with solid squares
data(Cars93, package="MASS")
plot(x=Cars93$Horsepower, y=Cars93$MPG.city, pch=15)

# Create index3, pointing to 3-cylinder cars
index3 <- which(Cars93$Cylinders == 3)

# Add text giving names of cars next to data points
text(x = Cars93$Horsepower[index3], 
     y = Cars93$MPG.city[index3],
     labels = Cars93$Make[index3], adj = 0)

# Plot MPG.city vs. Horsepower as open circles
data(Cars93, package="MASS")
plot(x=Cars93$Horsepower, y=Cars93$MPG.city, pch=1)

# Create index3, pointing to 3-cylinder cars
index3 <- which(Cars93$Cylinders == 3)

# Highlight 3-cylinder cars as solid circles
points(x=Cars93$Horsepower[index3], y=Cars93$MPG.city[index3], pch=16)

# Add car names, offset from points, with larger bold text
text(x = Cars93$Horsepower[index3], 
     y = Cars93$MPG.city[index3],
     labels = Cars93$Make[index3], adj = -0.2, cex=1.2, font=4)

# Plot Gas vs. Temp as solid triangles
data(whiteside, package="MASS")
plot(x=whiteside$Temp, y=whiteside$Gas, pch=17)

# Create indexB, pointing to "Before" data
indexB <- which(whiteside$Insul == "Before")

# Create indexA, pointing to "After" data
indexA <- which(whiteside$Insul == "After")

# Add "Before" text in blue, rotated 30 degrees, 80% size
text(x = whiteside$Temp[indexB], y = whiteside$Gas[indexB],
     labels = "Before", col = "blue", srt = 30, cex = 0.8)

# Add "After" text in red, rotated -20 degrees, 80% size
text(x = whiteside$Temp[indexA], y = whiteside$Gas[indexA],
     labels = "After", col = "red", srt = -20, cex = 0.8)

# Set up and label empty plot of Gas vs. Temp
data(whiteside, package="MASS")
plot(x=whiteside$Temp, y=whiteside$Gas,
     type = "n", xlab = "Outside temperature",
     ylab = "Heating gas consumption")

# Create indexB, pointing to "Before" data
indexB <- which(whiteside$Insul == "Before")

# Create indexA, pointing to "After" data
indexA <- which(whiteside$Insul == "After")

# Add "Before" data as solid triangles
points(x=whiteside$Temp[indexB], y=whiteside$Gas[indexB], pch=17)

# Add "After" data as open circles
points(x=whiteside$Temp[indexA], y=whiteside$Gas[indexA], pch=1)

# Add legend that identifies points as "Before" and "After"
legend("topright", pch = c(17, 1), 
       legend = c("Before", "After"))

# Create a boxplot of sugars by shelf value, without axes
data(UScereal, package="MASS")
boxplot(sugars ~ shelf, data=UScereal, axes=FALSE)

# Add a default y-axis to the left of the boxplot
axis(side = 2)

# Add an x-axis below the plot, labelled 1, 2, and 3
axis(side = 1, at = c(1, 2, 3))

# Add a second x-axis above the plot
axis(side = 3, at = c(1, 2, 3),
     labels = c("floor", "middle", "top"))

# Create a scatterplot of MPG.city vs. Horsepower
data(Cars93, package="MASS")
plot(x=Cars93$Horsepower, y=Cars93$MPG.city)

# Call supsmu() to generate a smooth trend curve, with default bass
trend1 <- supsmu(x=Cars93$Horsepower, y=Cars93$MPG.city)

# Add this trend curve to the plot
lines(trend1)

# Call supsmu() for a second trend curve, with bass = 10
trend2 <- supsmu(x=Cars93$Horsepower, y=Cars93$MPG.city, bass=10)

# Add this trend curve as a heavy, dotted line
lines(trend2, lty=3, lwd=2)

Visual complexity – too much detail, or too many arrayed plots, defeat the goals of explanatory analysis:

• Useful visualizations do not show too many details
  
• Multiple scatterplots on the same axes can be generated using matplot(), a function from base
  
• Generally best to err on the side of showing too little (as opposed to too much) detail

Multiple plot arrays (such as through par(mfrow)) can help show much detail:

• The plot() and pairs() functions will often do this automatically
  
• The par(pty=) will generate plots that use maximum space for “m” (the default), and plots that are square for “s”

Plot arrays can also be setup (more work, but more flexibility) using the layout() function:

• Basically, a matrix is created listing where each plot number should be placed (1 is for the first plot, 2 is for the second plot, 0 is for a blank space, etc.)
  
• Then, the layout() function is called on this matrix
  
• A future call to either mfrow() or layout() will override this
  
• The layout.show(n=) will show where the plots will go

Example code includes:

# Compute the number of plots to be displayed
data(Cars93, package="MASS")
ncol(Cars93)^2

## [1] 729

# Plot the array of scatterplots (not run - it is a mess, though!)
# plot(Cars93)


# Construct the vector keep_vars
keep_vars <- c("calories", "protein", "fat",
               "fibre", "carbo", "sugars")

# Use keep_vars to extract the desired subset of UScereal
data(UScereal, package="MASS")
df <- UScereal[, keep_vars]

# Set up a two-by-two plot array
par(mfrow=c(2, 2))

# Use matplot() to generate an array of two scatterplots
matplot(df$calories, df[, c("protein", "fat")], xlab="calories", ylab="")

# Add a title
title("Two scatterplots")

# Use matplot() to generate an array of three scatterplots
matplot(df$calories, df[, c("protein", "fat", "fibre")], xlab="calories", ylab="")

# Add a title
title("Three scatterplots")

# Use matplot() to generate an array of four scatterplots
matplot(df$calories, df[, c("protein", "fat", "fibre", "carbo")], xlab="calories", ylab="")

# Add a title
title("Four scatterplots")

# Use matplot() to generate an array of five scatterplots
matplot(df$calories, df[, c("protein", "fat", "fibre", "carbo", "sugars")], xlab="calories", ylab="")

# Add a title
title("Five scatterplots")

par(mfrow=c(1, 1))


# Create mfr_table of manufacturer frequencies
data(Cars93, package="MASS")
mfr_table <- table(Cars93$Manufacturer)

## NEED TO COMMENT THESE OUT - DEPENDENCY NOT AVAILABLE
# Create the default wordcloud from this table
# wordcloud(words = names(mfr_table), 
#           freq = as.numeric(mfr_table), 
#           scale = c(2, 0.25)
#           )

# Change the minimum word frequency
# wordcloud(words = names(mfr_table), 
#           freq = as.numeric(mfr_table), 
#           scale = c(2, 0.25), 
#           min.freq = 1
#           )

# Create model_table of model frequencies
model_table <- table(Cars93$Model)

# Create the wordcloud of all model names with smaller scaling
# wordcloud(words = names(model_table), 
#           freq = as.numeric(model_table), 
#           scale = c(0.75, 0.25), 
#           min.freq = 1
#           )


# Set up a two-by-two plot array
par(mfrow=c(2, 2))

# Plot y1 vs. x1 
plot(anscombe$x1, anscombe$y1)

# Plot y2 vs. x2
plot(anscombe$x2, anscombe$y2)

# Plot y3 vs. x3
plot(anscombe$x3, anscombe$y3)

# Plot y4 vs. x4
plot(anscombe$x4, anscombe$y4)

par(mfrow=c(1, 1))


# Define common x and y limits for the four plots
xmin <- min(anscombe[, c("x1", "x2", "x3", "x4")])
xmax <- max(anscombe[, c("x1", "x2", "x3", "x4")])
ymin <- min(anscombe[, c("y1", "y2", "y3", "y4")])
ymax <- max(anscombe[, c("y1", "y2", "y3", "y4")])

# Set up a two-by-two plot array
par(mfrow=c(2, 2))

# Plot y1 vs. x1 with common x and y limits, labels & title
plot(anscombe$x1, anscombe$y1,
     xlim = c(xmin, xmax),
     ylim = c(ymin, ymax),
     xlab = "x value", ylab = "y value",
     main = "First dataset")

# Do the same for the y2 vs. x2 plot
plot(anscombe$x2, anscombe$y2,
     xlim = c(xmin, xmax),
     ylim = c(ymin, ymax),
     xlab = "x value", ylab = "y value",
     main = "Second dataset")

# Do the same for the y3 vs. x3 plot
plot(anscombe$x3, anscombe$y3,
     xlim = c(xmin, xmax),
     ylim = c(ymin, ymax),
     xlab = "x value", ylab = "y value",
     main = "Third dataset")

# Do the same for the y4 vs. x4 plot
plot(anscombe$x4, anscombe$y4,
     xlim = c(xmin, xmax),
     ylim = c(ymin, ymax),
     xlab = "x value", ylab = "y value",
     main = "Fourth dataset")

par(mfrow=c(1, 1))


# Set up a two-by-two plot array
par(mfrow=c(2, 2))

# Plot the raw duration data
data(geyser, package="MASS")
plot(geyser$duration, main="Raw data")

# Plot the normalized histogram of the duration data
MASS::truehist(geyser$duration, main="Histogram")

# Plot the density of the duration data
plot(density(geyser$duration), main="Density")

# Construct the normal QQ-plot of the duration data
car::qqPlot(geyser$duration, main="QQ-plot")

par(mfrow=c(1, 1))


# Define row1, row2, row3 for plots 1, 2, and 3
row1 <- c(0, 1)
row2 <- c(2, 0)
row3 <- c(0, 3)

# Use the matrix function to combine these rows into a matrix
layoutMatrix <- matrix(c(row1, row2, row3), 
                       byrow = TRUE, nrow = 3)

# Call the layout() function to set up the plot array
layout(layoutMatrix)

# Show where the three plots will go 
layout.show(n=3)

# Set up the plot array
layout(layoutMatrix)

# Construct vectors indexB and indexA
data(whiteside, package="MASS")
indexB <- which(whiteside$Insul == "Before")
indexA <- which(whiteside$Insul == "After")

# Create plot 1 and add title
plot(whiteside$Temp[indexB], whiteside$Gas[indexB],
     ylim = c(0, 8)
     )
title("Before data only")

# Create plot 2 and add title
plot(whiteside$Temp, whiteside$Gas,
     ylim = c(0, 8)
     )
title("Complete dataset")

# Create plot 3 and add title
plot(whiteside$Temp[indexA], whiteside$Gas[indexA],
     ylim = c(0, 8)
     )
title("After data only")

# Create row1, row2, and layoutVector
row1 <- c(1, 0, 0)
row2 <- c(0, 2, 2)
layoutVector <- c(row1, row2, row2)

# Convert layoutVector into layoutMatrix
layoutMatrix <- matrix(layoutVector, byrow = TRUE, nrow = 3)

# Set up the plot array
layout(layoutMatrix)

# Plot scatterplot
data(Boston, package="MASS")
plot(Boston$rad, Boston$zn)

# Plot sunflower plot
sunflowerplot(Boston$rad, Boston$zn)

par(mfrow=c(1, 1))

Chapter 5 - Advanced Customization:

Most R plotting functions are called for their side-effects rather than their return values:

• However, some R plotting functions return invisible outputs that can be piped to a variable (e.g., barplot() returning the midpoints)
  
• The symbols() function in base R plotting allows for a third variable (e.g., using a third variable for squares= or bg= or the like)
  
• The files can be saved using png(); ; dev.off()

Using color effectively helps convey information to the reader

• Caution that this may not be effective for communicating with the color-blind
  
• Reproductions in B/W will minimize/eliminate any communication impact of color
  
• Over-use of colors (including inability to distinguish) is bad (“ideally about six . . . Hopefully no more than twelve . . . Absolutely no more than twenty . . .”)
  
• In order of preference, colors are “red”, “green”, “yellow”, “blue”, “black”, “white”, “pink”, “cyan”, “gray”, “orange”, “brown”, “purple”

Other graphics systems in R can expand significantly on the base R capabilities

• The systems tend to be incompatible with one another
  
• Base R is flexible, good for exploratory analysis, and easy to learn
  
• The grid graphics system in grid() provides much better control of low-level details, but at the price of a steeper learning curve
  
• The lattice graphics system is built on grid(), allowing for features such as the conditional scatterplot
  
• The ggplot2 package is also based on grid(), again allowing for many additional customizations (allows for building plots in stages)

Example code includes:

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.2.5

# Create a table of Cylinders frequencies
data(Cars93, package="MASS")
tbl <- table(Cars93$Cylinders)

# Generate a horizontal barplot of these frequencies
mids <- barplot(tbl, horiz = TRUE,
                col = "transparent",
                names.arg = ""
                )

# Add names labels with text()
text(20, mids, names(tbl))

# Add count labels with text()
text(35, mids, as.numeric(tbl))

# Call symbols() to create the default bubbleplot
symbols(Cars93$Horsepower, Cars93$MPG.city,
        circles = Cars93$Cylinders
        )

# Repeat, with the inches argument specified
symbols(Cars93$Horsepower, Cars93$MPG.city,
        circles = Cars93$Cylinders, 
        inches=0.2
        )

# Call png() with the name of the file we want to create
png("bubbleplot.png")

# Re-create the plot from the last exercise
symbols(Cars93$Horsepower, Cars93$MPG.city,
        circles = Cars93$Cylinders,
        inches = 0.2)

# Save our file and return to our interactive session
dev.off()

## png 
##   2

# Verify that we have created the file
list.files(pattern = "png")

## [1] "bubbleplot.png" "population.png"

# Iliinsky and Steele color name vector
IScolors <- c("red", "green", "yellow", "blue",
              "black", "white", "pink", "cyan",
              "gray", "orange", "brown", "purple")

# Create the data for the barplot
barWidths <- c(rep(2, 6), rep(1, 6))

# Recreate the horizontal barplot with colored bars
barplot(rev(barWidths), horiz = TRUE,
        col = rev(IScolors), axes = FALSE,
        names.arg = rev(IScolors), las = 1)

# Iliinsky and Steele color name vector
IScolors <- c("red", "green", "yellow", "blue",
              "black", "white", "pink", "cyan",
              "gray", "orange", "brown", "purple")

# Create the colored bubbleplot
symbols(Cars93$Horsepower, Cars93$MPG.city, 
        circles = Cars93$Cylinders, inches = 0.2, 
        bg = IScolors[as.factor(Cars93$Cylinders)])

# Create a table of Cylinders by Origin
tbl <- table(Cars93$Cylinders, Cars93$Origin)

# Create the default stacked barplot
barplot(tbl)

# Enhance this plot with color
barplot(tbl, col=IScolors[1:6])

# Use the data() function to load the dataCar data frame
data(dataCar, package="insuranceData")

# Generate the default tableplot() display
tabplot::tableplot(dataCar)

# Use xyplot() to construct the conditional scatterplot
data(UScereal, package="MASS")
lattice::xyplot(UScereal$calories ~ UScereal$sugars | UScereal$shelf)

# Create the basic plot (not displayed): basePlot
basePlot <- ggplot(Cars93, aes(x = Horsepower, y = MPG.city))

# Display the basic scatterplot
basePlot + 
  geom_point()

# Color the points by Cylinders value
basePlot + 
  geom_point(col = IScolors[Cars93$Cylinders])

# Make the point sizes also vary with Cylinders value
basePlot + 
  geom_point(col = IScolors[Cars93$Cylinders], 
             size = as.numeric(Cars93$Cylinders))

Data Visualization (ggplot2 part 1)

Data visulaization is the combination of Statistics and Design:

• Aids in communication and perception
  
• Exploratory plots are designed for a very small audience (just yourself even) and can be very dense
  
• Explanatory plots are designed to synthesize and communicate broadly - very labor intensive to create

The Anscombe plot examples show four different datasets explained by the identical linear model. This reinforces the importance of plotting the data prior to running analyses and drawing conclusions.

The “Grammar of Graphics” is a plotting framework based on the book by Leland Wilkinson, “Grammar of Graphics” 2(1999). The gist is that graphics are made of distinct layers of grammatical elements. Meaningful plots are created through aesthetic mapping.

Essential Grammatical Elements include:

• MANDATORY (this course) - Data (dataset for plotting), Aesthetics (scales for mapping the data), Geometries (visual elements)
  
• OPTIONAL (next course) - Facets (plotting small multiples), Statistics, Coordinates (space for plotting), Themes (non-data ink)

The ggplot2 package was one of the first developed and designed by Hadley Wickham. It implements the “Grammar of Graphics” in R, for example with:

• Data: iris
  
• Aesthetic: x=Sepal.Length, y=Sepal.Width
  
• Geometries: geom_jitter(alpha = 0.6)
  
• Facets: facet_grid(. ~ Species)
  
• Statistics: stat_smooth(method = “lm”, se = F, col=“red”)
  
• Coordinates/Themes: to be explored in a later course

The Anscombe data is good to have plotted for reference:

library(ggplot2)
data(anscombe)

ansX <- with(anscombe, c(x1, x2, x3, x4))
ansY <- with(anscombe, c(y1, y2, y3, y4))
ansType <- rep(1:4, each=nrow(anscombe))
ansFrame <- data.frame(x=ansX, y=ansY, series=factor(ansType))

# ggplot example for Anscombe data
ggplot(ansFrame, aes(x=x, y=y)) + 
    geom_point() + 
    geom_smooth(method="lm", col="red", se=FALSE, fullrange=TRUE) + 
    facet_wrap(~ series, nrow=2)

As well, the basic example code from above is useful to explore:

data(iris)

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width)) + 
    geom_jitter(alpha = 0.6) + 
    facet_grid(. ~ Species) + 
    stat_smooth(method = "lm", se = FALSE, col="red")

Some additional basic ggplot syntax includes (cached, since plotting each point of the diamonds dataset is taxing for the graphics):

# Explore the mtcars data frame with str()
data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# Execute the following command
ggplot(mtcars, aes(x = cyl, y = mpg)) +
  geom_point()

# Change the command below so that cyl is treated as factor
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_point()

# A scatter plot has been made for you
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

# Replace ___ with the correct vector
ggplot(mtcars, aes(x = wt, y = mpg, col = disp)) +
  geom_point()

# Replace ___ with the correct vector
ggplot(mtcars, aes(x = wt, y = mpg, size = disp)) +
  geom_point()

# Explore the diamonds data frame with str()
data(diamonds)
str(diamonds)

## Classes 'tbl_df', 'tbl' and 'data.frame':    53940 obs. of  10 variables:
##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...

# Add geom_point() with +
ggplot(diamonds, aes(x = carat, y = price)) + geom_point()

# Add geom_point() and geom_smooth() with +
ggplot(diamonds, aes(x = carat, y = price)) + geom_point() + geom_smooth()

## `geom_smooth()` using method = 'gam'

# The plot you created in the previous exercise
ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point() +
  geom_smooth()

## `geom_smooth()` using method = 'gam'

# Copy the above command but show only the smooth line
ggplot(diamonds, aes(x = carat, y = price)) +
  geom_smooth()

## `geom_smooth()` using method = 'gam'

# Copy the above command and assign the correct value to col in aes()
ggplot(diamonds, aes(x = carat, y = price, col=clarity)) +
  geom_smooth()

## `geom_smooth()` using method = 'gam'

# Keep the color settings from previous command. Plot only the points with argument alpha.
ggplot(diamonds, aes(x = carat, y = price, col=clarity)) +
  geom_point(alpha = 0.4)

# Create the object containing the data and aes layers: dia_plot
dia_plot <- ggplot(diamonds, aes(x = carat, y=price))

# Add a geom layer with + and geom_point()
dia_plot + geom_point()

# Add the same geom layer, but with aes() inside
dia_plot + geom_point(aes(col = clarity))

set.seed(1)
# The dia_plot object has been created for you
dia_plot <- ggplot(diamonds, aes(x = carat, y = price))

# Expand dia_plot by adding geom_point() with alpha set to 0.2
dia_plot <- dia_plot + geom_point(alpha = 0.2)

# Plot dia_plot with additional geom_smooth() with se set to FALSE
dia_plot + geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'gam'

# Copy the command from above and add aes() with the correct mapping to geom_smooth()
dia_plot + geom_smooth(se = FALSE, aes(col = clarity))

## `geom_smooth()` using method = 'gam'

Data Layer - How data structure influences plots (ggplot2 vs. base):

• Scatterplot in base: plot(iris\(Sepal.Length, iris\)Sepal.width); points(iris\(Petal.Length, iris\)Petal.Width, col=“red”)
  
• Many limitations to the base approach, including
  • Plot does not get redrawn, meaning some of the Petal Length/Width end up “off plot”
    
  • Plot is drawn solely as an image; it is not an object that we can retain/manipulate
    
  • Need to manually add/adjust the legend, a potential source for errors
    
  • No unified framework for plotting (different functions for each plot type)
    Differences in ggplot
    
  • All plots are always called with ggplot(, aes()) + geom_(), plus additional layers as needed
    
  • The ggplot can be stored as an object using the assignment operator ( <- )
    
• Note that the base package is generally fine for a very simple plot

Can add some additional points (similar to points() in base, but with axes rescaling for you):

• ggplot() + geom_point() + geom_point(aes(), col=“red”)
  • Plotting space is adjusted
    
  • This is due to ggplot2 having created an object (which can thus be manipulated)
    
• While the above works, it is really a misuse of the grammar of graphics - “please never do this!”
  • For example, there is no legend, and the axis labels are incorrect
    
• In reality, the X axis should be “length” and the Y axis should be width
  • Suppose that you make iris.wide, a 300x4 consisting of Species, Part (Petal, Sepal), Length, Width) instead of the original 150x5
    
  • So now, color can be based on Part, with X = Length and Y = Width
    
• When the data is made tidy, there are many advantages in the plotting
  • Tidy data for iris is format Species - Part (Sepal or Petal) - Measure (Length or Width) - Value

Example code includes:

data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# Plot the correct variables of mtcars
plot(mtcars$wt, mtcars$mpg, col=mtcars$cyl)

# Change cyl inside mtcars to a factor
mtcars$cyl <- as.factor(mtcars$cyl)

# Make the same plot as in the first instruction
plot(mtcars$wt, mtcars$mpg, col=mtcars$cyl)

# Use lm() to calculate a linear model and save it as carModel
carModel <- lm(mpg ~ wt, data = mtcars)

# Call abline() with carModel as first argument and set lty to 2
abline(carModel, lty=2)

# Plot each subset efficiently with lapply
# You don't have to edit this code
lapply(mtcars$cyl, function(x) {
  abline(lm(mpg ~ wt, mtcars, subset = (cyl == x)), col = x)
  })

## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## NULL
## 
## [[4]]
## NULL
## 
## [[5]]
## NULL
## 
## [[6]]
## NULL
## 
## [[7]]
## NULL
## 
## [[8]]
## NULL
## 
## [[9]]
## NULL
## 
## [[10]]
## NULL
## 
## [[11]]
## NULL
## 
## [[12]]
## NULL
## 
## [[13]]
## NULL
## 
## [[14]]
## NULL
## 
## [[15]]
## NULL
## 
## [[16]]
## NULL
## 
## [[17]]
## NULL
## 
## [[18]]
## NULL
## 
## [[19]]
## NULL
## 
## [[20]]
## NULL
## 
## [[21]]
## NULL
## 
## [[22]]
## NULL
## 
## [[23]]
## NULL
## 
## [[24]]
## NULL
## 
## [[25]]
## NULL
## 
## [[26]]
## NULL
## 
## [[27]]
## NULL
## 
## [[28]]
## NULL
## 
## [[29]]
## NULL
## 
## [[30]]
## NULL
## 
## [[31]]
## NULL
## 
## [[32]]
## NULL

# This code will draw the legend of the plot
# You don't have to edit this code
legend(x = 5, y = 33, legend = levels(mtcars$cyl), 
       col = 1:3, pch = 1, bty = "n")

# Plot 1: add geom_point() to this command to create a scatter plot
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) +
  geom_point()  # Fill in using instructions Plot 1

# Plot 2: include the lines of the linear models, per cyl
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) +
  geom_point() + # Copy from Plot 1
  geom_smooth(method="lm", se=FALSE)   # Fill in using instructions Plot 2

# Plot 3: include a lm for the entire dataset in its whole
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) +
  geom_point() + # Copy from Plot 2
  geom_smooth(method="lm", se=FALSE) + # Copy from Plot 2
  geom_smooth(aes(group = 1), method="lm", se=FALSE, linetype = 2)   # Fill in using instructions Plot 3

data(iris)
str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

# Option 1
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  geom_point(aes(x = Petal.Length, y = Petal.Width), col = "red")

# DS code to match up to lecturer data set formats
data(iris)
longIris <- tidyr::gather(iris, Type, Measure, -Species)
intIris <- tidyr::separate(longIris, Type, c("Part", "Metric"))
intIris$rowNum <- c(1:150, 1:150, 151:300, 151:300)
iris.wide <- tidyr::spread(intIris, Metric, Measure)
iris.tidy <- dplyr::select(dplyr::mutate(intIris, Value=Measure, Measure=Metric), Species, Part, Measure, Value)

# Option 2
ggplot(iris.wide, aes(x = Length, y = Width, col = Part)) +
  geom_point()

# Consider the structure of iris, iris.wide and iris.tidy (in that order)
str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

str(iris.wide)

## 'data.frame':    300 obs. of  5 variables:
##  $ Species: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Part   : chr  "Petal" "Petal" "Petal" "Petal" ...
##  $ rowNum : int  151 152 153 154 155 156 157 158 159 160 ...
##  $ Length : num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Width  : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...

str(iris.tidy)

## 'data.frame':    600 obs. of  4 variables:
##  $ Species: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Part   : chr  "Sepal" "Sepal" "Sepal" "Sepal" ...
##  $ Measure: chr  "Length" "Length" "Length" "Length" ...
##  $ Value  : num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...

# Think about which dataset you would use to get the plot shown right
# Fill in the ___ to produce the plot given to the right
ggplot(iris.tidy, aes(x = Species, y = Value, col = Part)) +
  geom_jitter() +
  facet_grid(. ~ Measure)

# Load the tidyr package
library(tidyr)

# Fill in the ___ to produce to the correct iris.tidy dataset
iris.tidy <- iris %>%
  gather(key, Value, -Species) %>%
  separate(key, c("Part", "Measure"), "\\.") 

# Consider the head of iris, iris.wide and iris.tidy (in that order)
head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

head(iris.wide)

##   Species  Part rowNum Length Width
## 1  setosa Petal    151    1.4   0.2
## 2  setosa Petal    152    1.4   0.2
## 3  setosa Petal    153    1.3   0.2
## 4  setosa Petal    154    1.5   0.2
## 5  setosa Petal    155    1.4   0.2
## 6  setosa Petal    156    1.7   0.4

head(iris.tidy)

##   Species  Part Measure Value
## 1  setosa Sepal  Length   5.1
## 2  setosa Sepal  Length   4.9
## 3  setosa Sepal  Length   4.7
## 4  setosa Sepal  Length   4.6
## 5  setosa Sepal  Length   5.0
## 6  setosa Sepal  Length   5.4

# Think about which dataset you would use to get the plot shown right
# Fill in the ___ to produce the plot given to the right
ggplot(iris.wide, aes(x = Length, y = Width, col = Part)) +
  geom_jitter() +
  facet_grid(. ~ Species)

# Add column with unique ids (don't need to change)
iris$Flower <- 1:nrow(iris)

# Fill in the ___ to produce to the correct iris.wide dataset
iris.wide <- iris %>%
  gather(key, value, -Species, -Flower) %>%
  separate(key, c("Part", "Measure"), "\\.") %>%
  spread(Measure, value)

Visible aesthetics are the cornerstone of the ggplot:

• Specific to ggplot, “how things look” is an attribute rather than an aesthetic
  
• For ggplot, the aesthetic is just the mapping of the variables to the plots
  
• For example, on a scatterplot, the mapping of variables to the x-axis and the y-axis is an aesthetic (as is the mapping of a variable to color)
  • It is always a data frame COLUMN that is mapped on to an aesthetic
    
• Typical aesthetics include (n.b., many of these can function as EITHER aesthetic mappings OR attributes)
  • x - x-axis position
    
  • y - y-axis position
    
  • colour - colour of dots, outlines of other shapes
    
  • fill - fill colour
    
  • size - diameter of points, thickness of lines
    
  • alpha - transparency (0 = transparent, 1=opaque)
    
  • linetype - line dash pattern
    
  • labels - direct labels of an item (text on a plot or axes)
    
  • shape - shape
    
• Common mistake is to use attribute rather than aesthetic and/or to overwrite the aesthetic with the attribute

Modifying aesthetics:

• position “identity”" is the default for a scatterplot - data will be plotted directly as per the aesthetic mapping
  • geom_point(position = “identity”) is technically fine also, though not needed as it is the default
    
  • geom_point(position = “jitter”) is the same as geom_jitter()
    
  • Can also define the jitter, such as posn.j <- position_jitter(width = 0.1), followed later by geom_point(position = posn.j)
    
• scale functions
  • scale_x
    
  • scale_y
    
  • scale_color
    
  • scale_fill
    
  • scale_shape
    
  • scale_linetype
    
• limit functions (max and min for example)
  
• breaks
  
• expand (always a c(multiplicative, additive)), for creating gaps between the data and the axes
  
• labels (to explicitly define the labels) . . . Which can also just use the labs() function

Best practices for choosing among the aesthetics (though note that “there is a fair bit of creativity involved”):

• Jacques Bertin (“Seminology of Graphics”, 1967) and William Cleveland (“Perception of Visual Elements”, 1990s)
  
• Form should always follow function (what is the point of the graph)
  • Beautiful plots is not the primary objective
    
  • Never misrepresent or obscure (do not create plots just to confuse the issue)
    
  • Always some information loss when translating between raw data and visual representations
    
• Colors can be a confusing way to differentiate continuous variables (better for discrete variables)
  
• Different axes can create misinterpretations also
  
• Using shape without color can be very confusing also

Example code includes:

data(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$am <- as.factor(mtcars$am)

# Map cyl to y
ggplot(mtcars, aes(x=mpg, y=cyl)) + geom_point()

# Map cyl to x
ggplot(mtcars, aes(y=mpg, x=cyl)) + geom_point()

# Map cyl to col
ggplot(mtcars, aes(y=mpg, x=wt, col=cyl)) + geom_point()

# Change shape and size of the points in the above plot
ggplot(mtcars, aes(y=mpg, x=wt, col=cyl)) + geom_point(shape=1, size=4)

# Map cyl to fill
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) +
  geom_point()

# Change shape, size and alpha of the points in the above plot
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) +
  geom_point(shape=16, size=6, alpha=0.6)

# Map cyl to size
ggplot(mtcars, aes(y=mpg, x=wt, size=cyl)) + geom_point()

## Warning: Using size for a discrete variable is not advised.

# Map cyl to alpha
ggplot(mtcars, aes(y=mpg, x=wt, alpha=cyl)) + geom_point()

# Map cyl to shape 
ggplot(mtcars, aes(y=mpg, x=wt, shape=cyl)) + geom_point()

# Map cyl to labels
ggplot(mtcars, aes(y=mpg, x=wt, label=cyl)) + geom_text()

# Define a hexadecimal color
my_color <- "#123456"

# Set the color aesthetic 
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point()

# Set the color aesthetic and attribute 
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point(col = my_color)

# Set the fill aesthetic and color, size and shape attributes
ggplot(mtcars, aes(x=wt, y=mpg, fill=cyl)) + geom_point(size=10, shape=23, col=my_color)

# Expand to draw points with alpha 0.5
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_point(alpha=0.5)

# Expand to draw points with shape 24 and color yellow
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_point(shape=24, col="yellow")

# Expand to draw text with label x, color red and size 10
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_text(label="x", col="red", size=10)

# Map mpg onto x, qsec onto y and factor(cyl) onto col
ggplot(mtcars, aes(x=mpg, y=qsec, col=factor(cyl))) + geom_point()

# Add mapping: factor(am) onto shape
ggplot(mtcars, aes(x=mpg, y=qsec, col=factor(cyl), shape=factor(am))) + geom_point()

# Add mapping: (hp/wt) onto size
ggplot(mtcars, aes(x=mpg, y=qsec, col=factor(cyl), shape=factor(am), size=(hp/wt))) + geom_point()

# Basic scatter plot: wt on x-axis and mpg on y-axis; map cyl to col
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point(size=4)

# Hollow circles - an improvement
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point(size=4, shape=1)

# Add transparency - very nice
ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + geom_point(size=4, alpha=0.6)

Next, bar plots are examined using the same data:

cyl.am <- ggplot(mtcars, aes(x = factor(cyl), fill = factor(am)))

# Add geom (position = "stack" by default)
cyl.am + geom_bar()

# Fill - show proportion
cyl.am + 
  geom_bar(position = "fill")  

# Dodging - principles of similarity and proximity
cyl.am +
  geom_bar(position = "dodge") 

# Clean up the axes with scale_ functions
val = c("#E41A1C", "#377EB8")
lab = c("Manual", "Automatic")
cyl.am +
  geom_bar(position = "dodge") +
  scale_x_discrete("Cylinders") + 
  scale_y_continuous("Number") +
  scale_fill_manual("Transmission", 
                    values = val,
                    labels = lab) 

# Add a new column called group
mtcars$group <- 0

# Create jittered plot of mtcars: mpg onto x, group onto y
ggplot(mtcars, aes(x = mpg, y=group)) + geom_jitter()

# Change the y aesthetic limits
ggplot(mtcars, aes(x = mpg, y=group)) + geom_jitter() + scale_y_continuous(limits = c(-2, 2))

Further, the diamonds data set is explored to show techniques for minimizing over-plotting problems. Per previous, it is cached due to the lengthy plot times driven by the many data points:

data(diamonds)
str(diamonds)

## Classes 'tbl_df', 'tbl' and 'data.frame':    53940 obs. of  10 variables:
##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...

# Scatter plot: carat (x), price (y), clarity (col)
ggplot(diamonds, aes(x=carat, y=price, col=clarity)) + geom_point()

# Adjust for overplotting
ggplot(diamonds, aes(x=carat, y=price, col=clarity)) + geom_point(alpha = 0.5)

# Scatter plot: clarity (x), carat (y), price (col)
ggplot(diamonds, aes(y=carat, x=clarity, col=price)) + geom_point(alpha = 0.5)

# Dot plot with jittering
ggplot(diamonds, aes(y=carat, x=clarity, col=price)) + geom_point(alpha = 0.5, position="jitter")

The geometries layer includes the most common plot types:

• Scatter - point, jitter, abline
  
• Bar - histogram, bar, errorbar
  
• Line - line

Scatter plots examples - geom_point(), geom_jitter(), geom_abline():

• geom_point() requires that x and y be aesthetics, and has optional aesthetic/attribute for alpha, colour, fill, shape, and size
  
• Can specify the aesthetics inside the geom, meaning that the layered geoms may each have a different aesthetic if desired
  
• In general, the geom_point() will INHERIT from the ggplot() call, although:
  • If anything is explicitly called inside geom_point() that will override what is in the ggplot() statement
    
  • Interestingly, can even have geom_point(data=other_data), and that will use other_data but still INHERIT whatever aesthetics for x and y were chosen previously
    
• The shape argument is equivalent to the pch argument from base
  • Note that 21-25 have both fill and color which can be controlled independently
    
• Note that geom_vline() and geom_hline() will make vertical and horizontal lines
  
• Note that ggplot2 can often handle the statistics for you, and that mean without dispersion can tend to be misleading
  
• Always optimize shape, size, and alpha blending

Bar plots examples - histogram, bar, errorbar:

• When using geom_histogram, only x is required to be included as an aesthetic
  • This is actually binned, with a slightly different algorithm than hist() in base geom_histogram(binwidth=) # is excluded, will use range()/30
    
• The labels are the intervals, while the y-axis is the counts
  • geom_histogram(aes(y = ..density.. ), binwidth=0.1) # The ..density.. Asks R to look for the internal data frame created by ggplot() as part of the histogram
    
• For coloring, we need to use fill= since this is a shape to be filled (col would apply if these were points in a scatter-gram)
  • The col would only change the outside of the bars
    
• The default is for the bars to be stacked based on value (position = “identity”)
  • position = “dodge” means one bar per category
    
  • position = “fill” is a stacked bar chart to 100%
    
• The geom_bar is the superset that contains geom_histogram() as one of its components
  • If just geom_bar() is called, the result will look like geom_histogram(), though you can have a purely categorical variable as the aes(x=) component
    
  • The default argument for geom_bar() is stat=“bin”
    
  • If instead stat=“identity” is included, then the actual y value inside aes() will be plotted
    
• The geom_errorbar() requires its own aes() call inside it – covering ymin, ymax, and width
  
• The standard publication “dynamite plot” is strongly discouraged – rationale and alternatives to be explained in a future module

Line plots examples - line:

• The simplest line graph is set up just like a scatterplot, but with calling geom_line() rather than geom_point()
  
• Can add col= as part of the aesthetic, which will color the line by that variable
  
• The geom_area() call combined with a fill= in the aes() will provide a stacked line chart
  • If position = “fill” is called inside geom_area(), then it will be a stacked bar, showing proportional trends over time
    
• The geom_ribbon() call can put them all on the same scale, provided that ymin=0 is part of aes() inside geom_ribbon(), followed by alpha= so that they are not opaque overlaps

Example code from mtcars includes:

# mtcars point plots
# Plot the cyl on the x-axis and wt on the y-axis
ggplot(mtcars, aes(x=cyl, y=wt)) + geom_point()

# Use geom_jitter() instead of geom_point()
ggplot(mtcars, aes(x=cyl, y=wt)) + geom_jitter()

# Define the position object using position_jitter(): posn.j
posn.j <- position_jitter(width = 0.1)
  
# Use posn.j in geom_point()
ggplot(mtcars, aes(x=cyl, y=wt)) + geom_point(position = posn.j)

# mtcars bar plots
# Make a univariate histogram
ggplot(mtcars, aes(x=mpg)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Change the bin width to 1
ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 1)

# Change the y aesthetic to density
ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 1, aes(y=..density..))

# Custom color code
myBlue <- "#377EB8"

# Change the fill color to myBlue
ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth = 1, aes(y=..density..), fill=myBlue)

# Draw a bar plot of cyl, filled according to am
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar()

# Change the position argument to stack
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position="stack")

# Change the position argument to fill
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position="fill")

# Change the position argument to dodge
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position="dodge")

# Draw a bar plot of cyl, filled according to am
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar()

# Change the position argument to "dodge"
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position = "dodge")

# Define posn_d with position_dodge()
posn_d <- position_dodge(width=0.2)

# Change the position argument to posn_d
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position = posn_d)

# Use posn_d as position and adjust alpha to 0.6
ggplot(mtcars, aes(x=cyl, fill=am)) + geom_bar(position = posn_d, alpha=0.6)

# A basic histogram, add coloring defined by cyl 
ggplot(mtcars, aes(x=mpg, fill=cyl)) +
  geom_histogram(binwidth = 1)

# Change position to identity 
ggplot(mtcars, aes(x=mpg, fill=cyl)) +
  geom_histogram(binwidth = 1, position="identity")

# Change geom to freqpoly (position is identity by default) 
ggplot(mtcars, aes(x=mpg, col=cyl)) +
  geom_freqpoly(binwidth = 1, position="identity")

# Example of how to use a brewed color palette
ggplot(mtcars, aes(x = cyl, fill = am)) +
  geom_bar() +
  scale_fill_brewer(palette = "Set1")

# Basic histogram plot command
ggplot(mtcars, aes(mpg)) + 
  geom_histogram(binwidth = 1)

# Expand the histogram to fill using am
ggplot(mtcars, aes(x=mpg, fill=am)) + 
  geom_histogram(binwidth = 1)

# Change the position argument to "dodge"
ggplot(mtcars, aes(x=mpg, fill=am)) + 
  geom_histogram(binwidth = 1, position="dodge")

# Change the position argument to "fill"
ggplot(mtcars, aes(x=mpg, fill=am)) + 
  geom_histogram(binwidth = 1, position="fill")

## Warning: Removed 16 rows containing missing values (geom_bar).

# Change the position argument to "identity" and set alpha to 0.4
ggplot(mtcars, aes(x=mpg, fill=am)) + 
  geom_histogram(binwidth = 1, position="identity", alpha = 0.4)

# Change fill to cyl
ggplot(mtcars, aes(x=mpg, fill=cyl)) + 
  geom_histogram(binwidth = 1, position="identity", alpha = 0.4)

Next, a few examples are run from dataset car::Vocab (cached due to plotting size/time):

Vocab <- car::Vocab
str(Vocab)

## 'data.frame':    21638 obs. of  4 variables:
##  $ year      : int  2004 2004 2004 2004 2004 2004 2004 2004 2004 2004 ...
##  $ sex       : Factor w/ 2 levels "Female","Male": 1 1 2 1 2 2 1 2 2 1 ...
##  $ education : int  9 14 14 17 14 14 12 10 11 9 ...
##  $ vocabulary: int  3 6 9 8 1 7 6 6 5 1 ...

# Basic scatter plot of vocabulary (y) against education (x). Use geom_point()
ggplot(Vocab, aes(x=education, y=vocabulary)) + geom_point()

# Use geom_jitter() instead of geom_point()
ggplot(Vocab, aes(x=education, y=vocabulary)) + geom_jitter()

# Using the above plotting command, set alpha to a very low 0.2
ggplot(Vocab, aes(x=education, y=vocabulary)) + geom_jitter(alpha = 0.2)

# Using the above plotting command, set the shape to 1
ggplot(Vocab, aes(x=education, y=vocabulary)) + geom_jitter(alpha = 0.2, shape=1)

# Plot education on x and vocabulary on fill
# Use the default brewed color palette
ggplot(Vocab, aes(x = education, fill = vocabulary)) +
  geom_bar(position="fill") +
  scale_fill_brewer()

# Definition of a set of blue colors
blues <- brewer.pal(9, "Blues")

# Make a color range using colorRampPalette() and the set of blues
blue_range <- colorRampPalette(blues)

# Use blue_range to adjust the color of the bars, use scale_fill_manual()
ggplot(Vocab, aes(x = education, fill = factor(vocabulary))) +
  geom_bar(position = "fill") +
  scale_fill_manual(values = blue_range(11))

Lastly, a few additional plots are displayed:

# Print out head of economics
data(economics)
head(economics)

## Source: local data frame [6 x 6]
## 
##         date   pce    pop psavert uempmed unemploy
##       (date) (dbl)  (int)   (dbl)   (dbl)    (int)
## 1 1967-07-01 507.4 198712    12.5     4.5     2944
## 2 1967-08-01 510.5 198911    12.5     4.7     2945
## 3 1967-09-01 516.3 199113    11.7     4.6     2958
## 4 1967-10-01 512.9 199311    12.5     4.9     3143
## 5 1967-11-01 518.1 199498    12.5     4.7     3066
## 6 1967-12-01 525.8 199657    12.1     4.8     3018

# Plot unemploy as a function of date using a line plot
ggplot(economics, aes(x = date, y = unemploy)) + geom_line()

# Adjust plot to represent the fraction of total population that is unemployed
ggplot(economics, aes(x = date, y = unemploy/pop)) + geom_line()

recess <- data.frame(begin=as.Date(c(-31, 1400, 3652, 4199, 7486, 11382), origin="1970-01-01"), end=as.Date(c(304, 1885, 3834, 4687, 7729, 11627), origin="1970-01-01"))

ggplot(economics, aes(x = date, y = unemploy/pop)) +
  geom_line() + 
  geom_rect(data=recess, inherit.aes=FALSE, aes(xmin=begin, xmax=end, ymin=-Inf, ymax=+Inf), fill="red", alpha=0.2)

# Cannot find dataset . . . 
# Check the structure as a starting point
# str(fish.species)

# Use gather to go from fish.species to fish.tidy
# fish.tidy <- gather(fish.species, Species, Capture, -Year)

# Recreate the plot shown on the right
# ggplot(fish.tidy, aes(x = Year, y = Capture, col=Species)) + geom_line()

The qplot functionality is for making quick and dirty plots:

• qplot(x, y, data=) will make a scatterplot
  
• Can add col=, shape=, size= and the like
  
• Can add geom=“jitter” (or whatever) to be even more explicit
  
• Can add alpha=I(0.5) where the I() function means “inhibit”, preventing R from treating this as a new data point (somehow . . . )

Basically, the qplot() is nice for just a quick and dirty analysis, though it will have much less flexibility on a go-forward basis.

Example code for qplot includes:

# The old way (shown)
plot(mpg ~ wt, data = mtcars)

# Using ggplot:
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()

# Using qplot:
qplot(wt, mpg, data=mtcars)

# Categorical:
# cyl
qplot(wt, mpg, data=mtcars, size=cyl)

## Warning: Using size for a discrete variable is not advised.

# gear
qplot(wt, mpg, data=mtcars, size=gear)

# Continuous
# hp
qplot(wt, mpg, data=mtcars, col=hp)

# qsec
qplot(wt, mpg, data=mtcars, col=qsec)

# qplot() with x only
qplot(factor(cyl), data=mtcars)

# qplot() with x and y
qplot(factor(cyl), factor(vs), data=mtcars)

# qplot() with geom set to jitter manually
qplot(factor(cyl), factor(vs), data=mtcars, geom="jitter")

# Make a dot plot with ggplot
ggplot(mtcars, aes(x=cyl, y=wt, fill = factor(am))) + 
    geom_dotplot(stackdir="center", binaxis="y")

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

# qplot with geom "dotplot", binaxis = "y" and stackdir = "center"
qplot(cyl, wt, fill=factor(am), data=mtcars, geom="dotplot", binaxis="y", stackdir="center")

## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

Course #1 wrap-up comments:

• Start with “who is the intended audience” so thet form follows function
  
• Good plots include Grammatical Elements and Aesthetic Mappings
  • This course focused on three Grammatical Elements (Data, Aestehtics, and Geometries)
    
  • The Aesthetic Mappings can be thought of as scales for encoding elements
    
• Proper data formats and transformations make the appropriate information available for plotting
  • Recall the tidying for iris, iris.wide, and iris.tidy
    
  • The proper data depends on the plot that we want to create
    
• The aesthetic choice depends on both the variable type (continuous vs. discrete) as well as the ease for the audience to decode the meaning
  
• Common plot types include scatter, line, and bar
  
• Recall the difference in the aesthetics and the attributes
  
• Jitter or alpha can be good techniques for overcoming over-plotting (can use position= as an additional option)

A few wrap-up coding exercises for ggplot #1 include:

# Check out the head of ChickWeight
data(ChickWeight)
head(ChickWeight)

## Grouped Data: weight ~ Time | Chick
##   weight Time Chick Diet
## 1     42    0     1    1
## 2     51    2     1    1
## 3     59    4     1    1
## 4     64    6     1    1
## 5     76    8     1    1
## 6     93   10     1    1

# Use ggplot() for the second instruction
ggplot(ChickWeight, aes(x=Time, y=weight)) + geom_line(aes(group=Chick))

# Use ggplot() for the third instruction
ggplot(ChickWeight, aes(x=Time, y=weight, col=Diet)) + geom_line(aes(group=Chick))

# Use ggplot() for the last instruction
ggplot(ChickWeight, aes(x=Time, y=weight, col=Diet)) + geom_line(aes(group=Chick), alpha=0.3) + geom_smooth(lwd=2, se=FALSE)

## `geom_smooth()` using method = 'loess'

# Check out the structure of titanic
library(titanic)

## Warning: package 'titanic' was built under R version 3.2.5

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

detach(package:dplyr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

titanicFull <- titanic::titanic_train
str(titanicFull)

## 'data.frame':    891 obs. of  12 variables:
##  $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
##  $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
##  $ Name       : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
##  $ Sex        : chr  "male" "female" "female" "female" ...
##  $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
##  $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
##  $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
##  $ Ticket     : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
##  $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
##  $ Cabin      : chr  "" "C85" "" "C123" ...
##  $ Embarked   : chr  "S" "C" "S" "S" ...

titanic <- titanicFull %>% 
    select(Pclass, Sex, Survived, Age) %>% 
    filter(complete.cases(.))
str(titanic)

## 'data.frame':    714 obs. of  4 variables:
##  $ Pclass  : int  3 1 3 1 3 1 3 3 2 3 ...
##  $ Sex     : chr  "male" "female" "female" "female" ...
##  $ Survived: int  0 1 1 1 0 0 0 1 1 1 ...
##  $ Age     : num  22 38 26 35 35 54 2 27 14 4 ...

# Use ggplot() for the first instruction
ggplot(titanic, aes(x=factor(Pclass), fill=factor(Sex))) + 
    geom_bar(position = "dodge")

# Use ggplot() for the second instruction
ggplot(titanic, aes(x=factor(Pclass), fill=factor(Sex))) + 
    geom_bar(position = "dodge") + 
    facet_grid(. ~ Survived)

# Position jitter (use below)
posn.j <- position_jitter(0.5, 0)

# Use ggplot() for the last instruction
ggplot(titanic, aes(x=factor(Pclass), y=Age, col=factor(Sex))) + 
    geom_jitter(size=3, alpha=0.5, position=posn.j) + 
    facet_grid(. ~ Survived)

Data Visualization (ggplot2 part 2)

The second course expands on the remaining layers of ggplot2: Statistics, Coordinates, Facets, and Themes.

The statistics layer for ggplot2 has two basic components:

• Called from within a geom
  
• Called from outside a geom (independently)
  
• The stat functions all start with stat_ meaning that stat_bin() is the binning call for histograms
  • stat_bin() and geom_bar() are closely related
    
  • stat_smooth() and geom_smooth() are closely related – se is by defautl the 95% CI
    • The loess function is used for smallish (<1000) bins, with span= available as an option to change the window size
      
    • Can instead specifically call method=“lm” and the line will be based on OLS, with 95% CI defaulted in addition
      
    • Can also add the fullrange= call to request that the predictions be made for out-of-range data
      
• The relationships between the stat_ and geom_ help to 1) explain warnings/errors, and 2) provide help pages for best debugging/tuning parameters

The statistics can also be called independently (outside the geom):

• Note that Hmisc::smean.sdl(x, mult=1) will return mean, mean-1SD, mean+1SD as a vector
  • The default is mult=2, so Hmisc::smean() defaults to providing mean +/- 2SD
    
• Within ggplot, this is called as mean_sdl(), with the first argument the vector, and the second argument the mult=
  
• Can be called inside stat_summary(fun.data = mean_sdl, fun.args=list(mult=1))
  
• Alternately, can be called as stat_summary(fun.y = mean, geom=“point”) + stat_summary(fun.data = mean_sdl, fun.args=list(mult=1), geom=“errorbar”, width=0.1) # will make a point mean with errorbars
  
• Note that Hmisc::smean.cl.normal(x) will return mean, then 95% CI
  • Within ggplot, this is called as mean_cl_normal(x)
    
• Can use anything inside stat_summary provided that the output is aligned with what the specified geom will require
  
• The most common function calls are stat_summary(), stat_function(), and stat_qq()
  
• Now back to the MASS::mammals dataset for brain vs body
  • Can add stat_function(fun = dnorm, color=“red”, arg=list(mean = mean(mam.new\(body), sd = sd(mam.new\)body)) and the normal density will plot on top of the graph
    
  • The geom_rug() can be helpful for putting the rug underneath the histogram

Example code from mtcars includes:

# Explore the mtcars data frame with str()
data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# A scatter plot with LOESS smooth:
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_smooth()

## `geom_smooth()` using method = 'loess'

# A scatter plot with an ordinary Least Squares linear model:
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() + 
  geom_smooth(method = "lm")

# The previous plot, without CI ribbon:
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() + 
  geom_smooth(method = "lm", se=FALSE)

# The previous plot, without points:
ggplot(mtcars, aes(x = wt, y = mpg)) + 
    geom_smooth(method = "lm", se=FALSE)

# Define cyl as a factor variable
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
  geom_point() +
  stat_smooth(method = "lm", se = FALSE)

# Complete the following ggplot command as instructed
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
  geom_point() +
  stat_smooth(method = "lm", se = FALSE) + 
  stat_smooth(method = "lm", se = FALSE, aes(group=1))

# Plot 1: change the LOESS span
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  # Add span below 
  geom_smooth(se = FALSE, span=0.7)

## `geom_smooth()` using method = 'loess'

# Plot 2: Set the overall model to LOESS and use a span of 0.7
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
  geom_point() +
  stat_smooth(method = "lm", se = FALSE) +
  # Change method and add span below
  stat_smooth(method = "loess", aes(group = 1), 
              se = FALSE, col = "black", span=0.7)

# Plot 3: Set col to "All", inside the aes layer of stat_smooth()
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
  geom_point() +
  stat_smooth(method = "lm", se = FALSE) +
  stat_smooth(method = "loess",
              # Add col inside aes()
              aes(group = 1, col="All"), 
              # Remove the col argument below
              se = FALSE, span = 0.7)

# Plot 4: Add scale_color_manual to change the colors
myColors <- c(brewer.pal(3, "Dark2"), "black")
ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) +
  geom_point() +
  stat_smooth(method = "lm", se = FALSE, span = 0.75) +
  stat_smooth(method = "loess", 
              aes(group = 1, col="All"), 
              se = F, span = 0.7) +
  # Add correct arguments to scale_color_manual
  scale_color_manual("Cylinders", values=myColors)

# Display structure of mtcars
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# Convert cyl and am to factors:
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$am <- as.factor(mtcars$am)

# Define positions:
posn.d <- position_dodge(width = 0.1)
posn.jd <- position_jitterdodge(jitter.width = 0.1, dodge.width = 0.2)
posn.j <- position_jitter(width = 0.2)

# base layers:
wt.cyl.am <- ggplot(mtcars, aes(x=cyl, y=wt, col=am, fill=am, group=am))

# Plot 1: Jittered, dodged scatter plot with transparent points
wt.cyl.am +
  geom_point(position = posn.jd, alpha = 0.6)

# Plot 2: Mean and SD - the easy way
wt.cyl.am +
  geom_point(position = posn.jd, alpha = 0.6) + 
  stat_summary(fun.data=mean_sdl, fun.args=list(mult=1), position=posn.d)

# Plot 3: Mean and 95% CI - the easy way
wt.cyl.am +
  geom_point(position = posn.jd, alpha = 0.6) + 
  stat_summary(fun.data=mean_cl_normal, position=posn.d)

# Plot 4: Mean and SD - with T-tipped error bars - fill in ___
wt.cyl.am +
  stat_summary(geom = "point", fun.y = mean, 
               position = posn.d) +
  stat_summary(geom = "errorbar", fun.data = mean_sdl, 
               position = posn.d, fun.args = list(mult = 1), width = 0.1)

xx <- 1:100

# Function to save range for use in ggplot 
gg_range <- function(x) {
  # Change x below to return the instructed values
  data.frame(ymin = min(x), # Min
             ymax = max(x)
             ) # Max
}

gg_range(xx)

##   ymin ymax
## 1    1  100

# Required output:
#   ymin ymax
# 1    1  100

# Function to Custom function:
med_IQR <- function(x) {
  # Change x below to return the instructed values
  data.frame(y = median(x), # Median
             ymin = quantile(x, 0.25), # 1st quartile
             ymax = quantile(x, 0.75)
             )  # 3rd quartile
}

med_IQR(xx)

##        y  ymin  ymax
## 25% 50.5 25.75 75.25

# Required output:
#        y  ymin  ymax
# 25% 50.5 25.75 75.25

wt.cyl.am <- ggplot(mtcars, aes(x = cyl,y = wt, col = am, fill = am, group = am))

# Add three stat_summary calls to wt.cyl.am
wt.cyl.am + 
  stat_summary(geom = "linerange", fun.data = med_IQR, 
               position = posn.d, size = 3) +
  stat_summary(geom = "linerange", fun.data = gg_range, 
               position = posn.d, size = 3, 
               alpha = 0.4) +
  stat_summary(geom = "point", fun.y = median, 
               position = posn.d, size = 3, 
               col = "black", shape = "X")

Further examples (cached) from car::Vocab include:

Vocab <- car::Vocab

# Plot 1: Jittered scatter plot, add a linear model (lm) smooth:
ggplot(Vocab, aes(x = education, y = vocabulary)) +
  geom_jitter(alpha = 0.2) +
  stat_smooth(method="lm", se=FALSE)

# Plot 2: Only lm, colored by year
ggplot(Vocab, aes(x = education, y = vocabulary, col=factor(year))) +
  stat_smooth(method="lm", se=FALSE)

# Plot 3: Set a color brewer palette
ggplot(Vocab, aes(x = education, y = vocabulary, col=factor(year))) +
  stat_smooth(method="lm", se=FALSE) + 
  scale_color_brewer()

## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Blues is 9
## Returning the palette you asked for with that many colors

# Plot 4: Add the group, specify alpha and size
ggplot(Vocab, aes(x = education, y = vocabulary, col = year, group=factor(year))) +
  stat_smooth(method = "lm", se = FALSE, alpha=0.6, size=2) +
  scale_color_gradientn(colors = brewer.pal(9,"YlOrRd"))

# Use stat_quantile instead of stat_smooth:
ggplot(Vocab, aes(x = education, y = vocabulary, col = year, group = factor(year))) +
  stat_quantile(alpha = 0.6, size = 2) +
  scale_color_gradientn(colors = brewer.pal(9,"YlOrRd"))

## Warning: package 'quantreg' was built under R version 3.2.4

## Loading required package: SparseM

## 
## Attaching package: 'SparseM'

## The following object is masked from 'package:base':
## 
##     backsolve

## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x

## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique

## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x

## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique

## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique

# Set quantile to 0.5:
ggplot(Vocab, aes(x = education, y = vocabulary, col = year, group = factor(year))) +
  stat_quantile(alpha = 0.6, size = 2, quantiles=c(0.5)) +
  scale_color_gradientn(colors = brewer.pal(9,"YlOrRd"))

## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x
## Smoothing formula not specified. Using: y ~ x

## Warning in rq.fit.br(wx, wy, tau = tau, ...): Solution may be nonunique

# Plot with linear and loess model
p <- ggplot(Vocab, aes(x = education, y = vocabulary)) +
       stat_smooth(method = "loess", aes(col = "red"), se = F) +
       stat_smooth(method = "lm", aes(col = "blue"), se = F) +
       scale_color_discrete("Model", labels = c("red" = "LOESS", "blue" = "lm"))

# Add stat_sum
p + stat_sum()

# Add stat_sum and set size range
p + stat_sum() + scale_size(range = c(1, 10))

The coordinates layers control the plot dimensions:

• Typically using coord_, such as coord_cartesian()
  Examples for zooming in on a plot
  • scale_x_continuous(limits = )
    
  • xlim()
    
  • coord_cartesian(xlim = ) # has the benefit that there are no more warning messages; the whole plot is created, and then this specific zoom is selected
    
• Aspect ratios should often, but not always, be on a 1:1 basis
  • coord_fixed(xNum) # the height will now be on a scale of xNum:1 vs. the width

The facets are based on the concept of small multiples as per the Tufte book on “Visulaization of Quantitative Information” (1983):

• The concept is to split one large plot in to several smaller plots that each contain the same coordinate system
  
• facet_grid(rows ~ columns), so facet_grid(. ~ Species) will mean make a single row, and use Species as the columns
  • The facet is just the splitting up of the data based on a factor variable

Example code includes:

data(mtcars); 
mtcars$cyl <- as.factor(mtcars$cyl); 
mtcars$am <- as.factor(mtcars$am)


# Basic ggplot() command, coded for you
p <- ggplot(mtcars, aes(x = wt, y = hp, col = am)) + geom_point() + geom_smooth()

# Add scale_x_continuous
p + scale_x_continuous(limits = c(3,6), expand=c(0,0))

## `geom_smooth()` using method = 'loess'

## Warning: Removed 12 rows containing non-finite values (stat_smooth).

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 3.168

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 4e-006

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 3.168

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.002

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 3.572

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 4e-006

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4e-006

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger

## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)

## Warning: Removed 12 rows containing missing values (geom_point).

# The proper way to zoom in:
p + coord_cartesian(xlim=c(3, 6))

## `geom_smooth()` using method = 'loess'

data(iris)

# Complete basic scatter plot function
base.plot <- ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, col=Species)) +
               geom_jitter() +
               geom_smooth(method = "lm", se = FALSE)

# Plot base.plot: default aspect ratio
base.plot

# Fix aspect ratio (1:1) of base.plot
base.plot + coord_equal()

# Create stacked bar plot: thin.bar
thin.bar <- ggplot(mtcars, aes(x=1, fill=cyl)) +
              geom_bar()

# Convert thin.bar to pie chart
thin.bar + coord_polar(theta = "y")

# Create stacked bar plot: wide.bar
wide.bar <- ggplot(mtcars, aes(x=1, fill=cyl)) +
              geom_bar(width=1)

# Convert wide.bar to pie chart
wide.bar + coord_polar(theta="y")

# Basic scatter plot:
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point()

# Separate rows according to transmission type, am
p + facet_grid(am ~ .)

# Separate columns according to cylinders, cyl
p + facet_grid(. ~ cyl)

# Separate by both columns and rows 
p + facet_grid(am ~ cyl)

# Code to create the cyl_am col and myCol vector
mtcars$cyl_am <- paste(mtcars$cyl, mtcars$am, sep = "_")
myCol <- rbind(brewer.pal(9, "Blues")[c(3,6,8)],
               brewer.pal(9, "Reds")[c(3,6,8)])

# Basic scatter plot, add color scale:
ggplot(mtcars, aes(x = wt, y = mpg, col=cyl_am)) +
  geom_point() + scale_color_manual(values = myCol)

# Facet according on rows and columns.
ggplot(mtcars, aes(x = wt, y = mpg, col=cyl_am)) +
  geom_point() + scale_color_manual(values = myCol) + 
  facet_grid(gear ~ vs)

# Add more variables
ggplot(mtcars, aes(x = wt, y = mpg, col=cyl_am, size=disp)) +
  geom_point() + scale_color_manual(values = myCol) + 
  facet_grid(gear ~ vs)

library(dplyr)
detach(package:dplyr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

mamsleep <- tidyr::gather(ggplot2::msleep %>% 
                              mutate(total = sleep_total, rem=sleep_rem) %>%
                              select(vore, name, total, rem) %>% 
                              filter(!is.na(total), !is.na(rem)), 
                          sleep, time, -c(vore, name))

mamsleep$sleep <- factor(mamsleep$sleep, levels=c("total", "rem"))
str(mamsleep)

## Classes 'tbl_df', 'tbl' and 'data.frame':    122 obs. of  4 variables:
##  $ vore : chr  "omni" "herbi" "omni" "herbi" ...
##  $ name : chr  "Owl monkey" "Mountain beaver" "Greater short-tailed shrew" "Cow" ...
##  $ sleep: Factor w/ 2 levels "total","rem": 1 1 1 1 1 1 1 1 1 1 ...
##  $ time : num  17 14.4 14.9 4 14.4 8.7 10.1 5.3 9.4 10 ...

# Basic scatter plot
ggplot(mamsleep, aes(x=time, y=name, col=sleep)) + geom_point()

# Facet rows accoding to vore
ggplot(mamsleep, aes(x=time, y=name, col=sleep)) + geom_point() + facet_grid(vore ~ .)

# Specify scale and space arguments to free up rows
ggplot(mamsleep, aes(x=time, y=name, col=sleep)) + geom_point() + 
    facet_grid(vore ~ ., scale="free_y", space="free_y")

The themes layer controls all the “non-data ink” on your plot:

• This means the visual elements that are not part of the data; typically text, line, or rectangle
  • These are called as element_text(), element_line(), or element_rectangle()
    
  • The headers for the individual facets are referred to as “strip text”
    
• Text is all the titles, axis labels, legend labels, and the like
  
• Lines are the axis ticks, axis lines, panel grids, and the like
  
• Rectangles include legend background/key, panel background/border, plot/strip background
  
• Many of the attributes inherit from others
  • Everything in text inherits from text
    
  • Everything in line inherits from line
    
  • Everything in rect inherits from rect
    
  • Further, things like axis.text.x will inherit from axis
    
• The element_blank() is for removing everything
  • theme(text = element_blank(), line=element_blank(), rect=element_blank()) # will just plot the points

Often an objective to repeat the theme multiple times in the same presentation:

• Typical approach would be to save the theme layer as an object, then use it as needed
  
• There are also some built-in themes available for use
  • theme_classic()
    
  • library(ggthemes); theme_tufte()
    
  • original <- theme_update() # everything in theme_update is modified, with the original values stored in original
    
• Can also declare a theme_set() at the top of the code, and everything that follows will use that theme

Example code includes:

# Rough re-engineer of what is pre-defined as z
data(mtcars)
mtcars$cyl <- factor(mtcars$cyl)

myColors <- c(brewer.pal(9, "Blues"))[c(5, 7, 9)]

origZ <- ggplot(mtcars, aes(x=wt, y=mpg, col=cyl)) + 
         geom_point(size=2) + 
         geom_smooth(method="lm", se=FALSE) + 
         facet_wrap(~ cyl, nrow=1) + 
         scale_x_continuous("Weight (lb/1000)") + 
         scale_y_continuous("Miles / (US) gallon") +
         scale_color_manual("Cylinders", values=myColors)
z <- origZ


# Plot 1: change the plot background color to myPink:
myPink <- "#FEE0D2"
z + theme(plot.background = element_rect(fill = myPink))

# Plot 2: adjust the border to be a black line of size 3
z + theme(plot.background = element_rect(fill = myPink, color="black", size=3))

# Plot 3: set panel.background, legend.key, legend.background and strip.background to element_blank()
uniform_panels <- theme(panel.background = element_blank(), 
                        legend.key = element_blank(), 
                        legend.background=element_blank(), 
                        strip.background = element_blank())
z <- z + theme(plot.background = element_rect(fill = myPink, color="black", size=3)) + uniform_panels
z

# Extend z with theme() function and three arguments
z <- z + theme(panel.grid=element_blank(), axis.line=element_line(color="black"),
               axis.ticks=element_line(color="black")
               )
z

# Extend z with theme() function and four arguments
myRed <- "#99000D"
z <- z + theme(strip.text = element_text(size=16, color=myRed), 
               axis.title.y=element_text(color=myRed, hjust=0, face="italic"),
               axis.title.x=element_text(color=myRed, hjust=0, face="italic"), 
               axis.text=element_text(color="black")
               )
z

# Move legend by position
z + theme(legend.position = c(0.85, 0.85))

# Change direction
z + theme(legend.direction = "horizontal")

# Change location by name
z + theme(legend.position = "bottom")

# Remove legend entirely
z + theme(legend.position = "none")

# Increase spacing between facets
library(grid)
z + theme(panel.margin.x = unit(2, "cm"))

## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead

## Warning: `panel.margin.x` is deprecated. Please use `panel.spacing.x`
## property instead

# Add code to remove any excess plot margin space
z + theme(panel.margin.x = unit(2, "cm"), plot.margin = unit(c(0,0,0,0), "cm"))

## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead

## Warning: `panel.margin.x` is deprecated. Please use `panel.spacing.x`
## property instead

# Make z2 the same as origZ
z2 <- origZ

# Theme layer saved as an object, theme_pink
theme_pink <- theme(panel.background = element_blank(),
                    legend.key = element_blank(),
                    legend.background = element_blank(),
                    strip.background = element_blank(),
                    plot.background = element_rect(fill = myPink, color = "black", size = 3),
                    panel.grid = element_blank(),
                    axis.line = element_line(color = "black"),
                    axis.ticks = element_line(color = "black"),
                    strip.text = element_text(size = 16, color = myRed),
                    axis.title.y = element_text(color = myRed, hjust = 0, face = "italic"),
                    axis.title.x = element_text(color = myRed, hjust = 0, face = "italic"),
                    axis.text = element_text(color = "black"),
                    legend.position = "none")
  
# Apply theme_pink to z2
z2 + theme_pink

# Change code so that old theme is saved as old
old <- theme_update(panel.background = element_blank(),
             legend.key = element_blank(),
             legend.background = element_blank(),
             strip.background = element_blank(),
             plot.background = element_rect(fill = myPink, color = "black", size = 3),
             panel.grid = element_blank(),
             axis.line = element_line(color = "black"),
             axis.ticks = element_line(color = "black"),
             strip.text = element_text(size = 16, color = myRed),
             axis.title.y = element_text(color = myRed, hjust = 0, face = "italic"),
             axis.title.x = element_text(color = myRed, hjust = 0, face = "italic"),
             axis.text = element_text(color = "black"),
             legend.position = "none")

# Display the plot z2
z2

# Restore the old plot
theme_set(old)

# Load ggthemes package
library(ggthemes)

## Warning: package 'ggthemes' was built under R version 3.2.5

# Apply theme_tufte
z2 + theme_tufte()

# Apply theme_tufte, modified:
z2 + theme_tufte() + 
    theme(legend.position = c(0.9, 0.9), legend.title=element_text(face="italic", size=12), axis.title=element_text(face="bold", size=14))

Best practices for plotting and visulaization:

• The “dynamite plot” (bar plot with errorbars) is not recommended
  • Suggests that the data is normally distributed (even though that may not be the case)
    
  • Does not provide any information about the n per category
    
  • Bars also suggest that there is “data at 0”, which with many activities is patently untrue
    
  • Also no bars above the mean (where there is certainly data)
    
• One alternative is to just plot the data points using a jitter/alpha so they can all be seen
  • The error bars with the jittered points are a much cleaner visulaization of the data

Recall that pie charts are just bar charts wrapped on to polar coordinates:

• The pie chart is often prone to misues since people interpret it differently - angle vs. area vs. arc-length vs. etc.
  
• Best practice is to reserve the pie chart for where there is a large, macro difference and only looking at ~3 groups
  
• The better practice is the stacked bar that sums to 100%; no need to rotate it on to polar coordinates

Suggestion that heat maps are “one of the least effective forms of communication”:

• Perceptions of colors can change based on the context
  
• Tendency for the heat map to convey a “wow factor” even though there is precious little information communicated
  
• Much better to facet and dot-plot the data on a continuous axis
  • It is very good for exploratory analysis; may be too dense for publication
    
• Final choices will depend on the data density and the composition of the audience

Example code includes:

data(mtcars)
mtcars$cyl <- factor(mtcars$cyl)
mtcars$am <- factor(mtcars$am)
m <- ggplot(mtcars, aes(x = cyl, y = wt))


# Draw dynamite plot
m +
  stat_summary(fun.y = mean, geom = "bar", fill = "skyblue") +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "errorbar", width = 0.1)

# Base layers
m <- ggplot(mtcars, aes(x = cyl, y = wt, col = am, fill = am))

# Plot 1: Draw dynamite plot
m +
  stat_summary(fun.y = mean, geom = "bar") +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "errorbar", width = 0.1)

# Plot 2: Set position dodge in each stat function
m +
  stat_summary(fun.y = mean, geom = "bar", position = "dodge") +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), 
               geom = "errorbar", width = 0.1, position = "dodge")

# Set your dodge posn manually
posn.d <- position_dodge(0.9)

# Plot 3:  Redraw dynamite plot
m +
  stat_summary(fun.y = mean, geom = "bar", position = posn.d) +
  stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "errorbar", 
               width = 0.1, position = posn.d
               )

cyl_means <- tapply(mtcars$wt, mtcars$cyl, FUN=mean)
cyl_sd <- tapply(mtcars$wt, mtcars$cyl, FUN=sd)
cyl_ct <- tapply(mtcars$wt, mtcars$cyl, FUN=length)
mtcars.cyl <- data.frame(cyl=as.factor(names(cyl_means)), 
                         wt.avg=cyl_means, sd=cyl_sd, prop=cyl_ct/sum(cyl_ct)
                         )


# Base layers
m <- ggplot(mtcars.cyl, aes(x = cyl, y = wt.avg))

# Plot 1: Draw bar plot
m + geom_bar(stat = "identity", fill="skyblue")

# Plot 2: Add width aesthetic
m + geom_bar(stat = "identity", fill="skyblue", aes(width=prop))

## Warning: Ignoring unknown aesthetics: width

# Plot 3: Add error bars
m + geom_bar(stat = "identity", fill="skyblue", aes(width=prop)) +
    geom_errorbar(aes(ymin = wt.avg - sd, ymax = wt.avg + sd), width=0.1)

## Warning: Ignoring unknown aesthetics: width

ggplot(mtcars, aes(x = factor(1), fill = am)) +
  geom_bar(position = "fill", width=1) + 
  facet_grid(. ~ cyl) + 
  coord_polar(theta="y")

library(GGally)

## Warning: package 'GGally' was built under R version 3.2.5

## Warning: replacing previous import by 'utils::capture.output' when loading
## 'GGally'

## Warning: replacing previous import by 'utils::head' when loading 'GGally'

## Warning: replacing previous import by 'utils::installed.packages' when
## loading 'GGally'

## Warning: replacing previous import by 'utils::str' when loading 'GGally'

## 
## Attaching package: 'GGally'

## The following object is masked from 'package:dplyr':
## 
##     nasa

# All columns except am
group_by_am <- match("am", names(mtcars))
my_names_am <- (1:11)[-group_by_am]

# Basic parallel plot - each variable plotted as a z-score transformation
ggparcoord(mtcars, my_names_am, groupColumn = group_by_am, alpha = 0.8)

barley <- lattice::barley
str(barley)

## 'data.frame':    120 obs. of  4 variables:
##  $ yield  : num  27 48.9 27.4 39.9 33 ...
##  $ variety: Factor w/ 10 levels "Svansota","No. 462",..: 3 3 3 3 3 3 7 7 7 7 ...
##  $ year   : Factor w/ 2 levels "1932","1931": 2 2 2 2 2 2 2 2 2 2 ...
##  $ site   : Factor w/ 6 levels "Grand Rapids",..: 3 6 4 5 1 2 3 6 4 5 ...

# Create color palette
myColors <- brewer.pal(9, "Reds")

# Build the heat map from scratch
ggplot(barley, aes(x=year, y=variety, fill=yield)) + 
    geom_tile() + 
    facet_wrap(~ site, ncol=1) + 
    scale_fill_gradientn(colors = myColors)

# Line plots
ggplot(barley, aes(x=year, y=yield, col=variety, group=variety)) + 
    geom_line() + 
    facet_wrap(~ site, nrow=1)

ggplot(barley, aes(x=year, y=yield, col=site, group=site, fill=site)) + 
    stat_summary(fun.y = mean, geom="line") + 
    stat_summary(fun.data = mean_sdl, fun.args=list(mult=1), geom="ribbon", col=NA, alpha=0.1)

CHIS (California Health Information Study) - Descriptive Statistics Case Study:

• Data available at healthpolicy.ucla.edu
  
• Data set “adult” from the 2009 version, starts with 47614 x 536
  • Reduced data set “adult” from the 2009 version will have 44346 x 10
    • RBMI, BMI_P, RACEHPR2, SRSEX, SRAGE_P, MARIT2, AB1, ASTCUR, AB51, POVLL
      
    • Filtered to ethnicities White, Black, Asian, Latino

The mosaic plot is a sequence of rectangles, each sized based on the size of the group:

• X-axis is then the total number of individuals
  
• Mosaic plots are a good means of looking at chi-squared data
  
• Table can report on both the size and the underlying statistics
  • See for example the mosaic() command available in library vcd
    
  • Could then color each rectangle based on whether it has been over or under represented

Case study code - creating a Merimeko plot:

# TBD - need the raw data first!

Data Visualization (ggplot2 part 3)

Advanced course that builds on concepts from the previous modules:

• Chapter 1 - graphs for the statistically savvy (box plots, density plots, violin plots, etc.)
  
• Chapter 2 - graphs for very specific data types (large data, networks, etc.)
  
• Chapter 3 - graphing with maps (cartography) and animations
  
• Chapter 4 - internals of the ggplot2 package (grid, manipulations for efficiency)
  
• Chapter 5 - case studies (building geoms from scratch; and a Tufte-plot)

Refresher code from previous modules includes:

# Create movies_small
library(ggplot2movies)

## Warning: package 'ggplot2movies' was built under R version 3.2.5

set.seed(123)
movies_small <- movies[sample(nrow(movies), 1000), ]
movies_small$rating <- factor(round(movies_small$rating))

# Create movies_small
library(ggplot2movies)
set.seed(123)
movies_small <- movies[sample(nrow(movies), 1000), ]
movies_small$rating <- factor(round(movies_small$rating))

# Explore movies_small with str()
str(movies_small)

## Classes 'tbl_df' and 'data.frame':   1000 obs. of  24 variables:
##  $ title      : chr  "Fair and Worm-er" "Shelf Life" "House: After Five Years of Living" "Three Long Years" ...
##  $ year       : int  1946 2000 1955 2003 1963 1992 1999 1972 1994 1985 ...
##  $ length     : int  7 4 11 76 103 107 87 84 127 94 ...
##  $ budget     : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ rating     : Factor w/ 10 levels "1","2","3","4",..: 7 7 6 8 8 5 4 8 5 5 ...
##  $ votes      : int  16 11 15 11 103 28 105 9 37 28 ...
##  $ r1         : num  0 0 14.5 4.5 4.5 4.5 14.5 0 4.5 4.5 ...
##  $ r2         : num  0 0 0 0 4.5 0 4.5 0 4.5 0 ...
##  $ r3         : num  0 0 4.5 4.5 0 4.5 4.5 0 14.5 4.5 ...
##  $ r4         : num  0 0 4.5 0 4.5 4.5 4.5 0 4.5 14.5 ...
##  $ r5         : num  4.5 4.5 0 0 4.5 0 4.5 14.5 24.5 4.5 ...
##  $ r6         : num  4.5 24.5 34.5 4.5 4.5 0 14.5 0 4.5 14.5 ...
##  $ r7         : num  64.5 4.5 24.5 0 14.5 4.5 14.5 14.5 14.5 14.5 ...
##  $ r8         : num  14.5 24.5 4.5 4.5 14.5 24.5 14.5 24.5 14.5 14.5 ...
##  $ r9         : num  0 0 0 14.5 14.5 24.5 14.5 14.5 4.5 4.5 ...
##  $ r10        : num  14.5 24.5 14.5 44.5 44.5 24.5 14.5 44.5 4.5 24.5 ...
##  $ mpaa       : chr  "" "" "" "" ...
##  $ Action     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Animation  : int  1 0 0 0 0 0 0 0 0 0 ...
##  $ Comedy     : int  1 0 0 1 0 1 1 1 0 0 ...
##  $ Drama      : int  0 0 0 0 1 0 0 0 1 1 ...
##  $ Documentary: int  0 0 1 0 0 0 0 0 0 0 ...
##  $ Romance    : int  0 0 0 0 0 0 1 0 0 0 ...
##  $ Short      : int  1 1 1 0 0 0 0 0 0 0 ...

# Build a scatter plot with mean and 95% CI
ggplot(movies_small, aes(x = rating, y = votes)) +
  geom_point() +
  stat_summary(fun.data = "mean_cl_normal",
               geom = "crossbar",
               width = 0.2,
               col = "red") +
  scale_y_log10()

# Reproduce the plot
ggplot(diamonds, aes(x=carat, y=price, col=color)) + 
  geom_point(alpha=0.5, size=0.5, shape=16) + 
  scale_x_log10(limits=c(0.1, 10)) + 
  xlab(expression(log[10](Carat))) + 
  scale_y_log10(limits=c(100, 100000)) +  
  ylab(expression(log[10](Price))) + 
  scale_color_brewer(palette="YlOrRd") + 
  coord_equal() + 
  theme_classic()

# Add smooth layer and facet the plot
ggplot(diamonds, aes(x = carat, y = price, col = color)) +
  geom_point(alpha = 0.5, size = .5, shape = 16) +
  scale_x_log10(expression(log[10](Carat)), limits = c(0.1,10)) +
  scale_y_log10(expression(log[10](Price)), limits = c(100,100000)) +
  scale_color_brewer(palette = "YlOrRd") +
  coord_equal() +
  theme_classic() + 
  stat_smooth(method="lm") + 
  facet_grid(. ~ cut)

Box plots creation and usage (more for an academic audience). John Tukey - “Exploratory Data Analysis” (median, IQR, max, min, extremes outside of 1.5 * IQR). Whiskers are only drawn up to the “fence” (1.5 * IQR); everything above this is the outlier.

Example code includes:

# Add a boxplot geom
d <- ggplot(movies_small, aes(x = rating, y = votes)) +
  geom_point() +
  geom_boxplot() +
  stat_summary(fun.data = "mean_cl_normal",
               geom = "crossbar",
               width = 0.2,
               col = "red")

# Untransformed plot
d

# Transform the scale
d + scale_y_log10()

# Transform the coordinates (produces error in RStudio - commented out)
# d + coord_trans(y = "log10")

# Plot object p
p <- ggplot(diamonds, aes(x = carat, y = price))

# Use cut_interval
p + geom_boxplot(aes(group = cut_interval(carat, n=10)))

# Use cut_number
p + geom_boxplot(aes(group = cut_number(carat, n=10)))

# Use cut_width
p + geom_boxplot(aes(group = cut_width(carat, width=0.25)))

Density plots are a good way to describe univariate data. There are many statistics (e.g., PDF or Probability Density Function), including the “Kernel Density Estimate” (KDE) - “sum of bumps placed at the observations; kernel function determines the shape of the bumps, while window width, h, determines their width”:

• x <- c(0.0, 1.0, 1.1, 1.5, 1.9, 2.8, 2.9, 3.5)
  
• ggplot(data.frame(x=x), aes(x=x)) + geom_rug(col=“dark green”) + lims(x=c(-2, 6))
  
• The normal KDE would build a normal around each point, then aggregate all the normals as the distribution
  
• The mode of a continuous distribution is defined as the peak of the KDE
  
• Generally, the defaults can be used for the bandwidth, h, though it can be adjusted if wanted
  
• The plots can extend beyond the limits of the data, which is mathematically correct but may be off-putting
  • The geom_density() function will cut off at the observed maximum and minimum (the area under the curve will no longer be 1, though)

Example code includes:

# Set up keys
t_norm <- c(-0.560475646552213,-0.23017748948328,1.55870831414912,0.070508391424576,0.129287735160946,1.71506498688328,0.460916205989202,-1.26506123460653,-0.686852851893526,-0.445661970099958,1.22408179743946,0.359813827057364,0.400771450594052,0.11068271594512,-0.555841134754075,1.78691313680308,0.497850478229239,-1.96661715662964,0.701355901563686,-0.472791407727934,-1.06782370598685,-0.217974914658295,-1.02600444830724,-0.72889122929114,-0.625039267849257,-1.68669331074241,0.837787044494525,0.153373117836515,-1.13813693701195,1.25381492106993,0.426464221476814,-0.295071482992271,0.895125661045022,0.878133487533042,0.821581081637487,0.688640254100091,0.553917653537589,-0.0619117105767217,-0.305962663739917,-0.380471001012383,-0.694706978920513,-0.207917278019599,-1.26539635156826,2.16895596533851,1.20796199830499,-1.12310858320335,-0.402884835299076,-0.466655353623219,0.779965118336318,-0.0833690664718293,0.253318513994755,-0.028546755348703,-0.0428704572913161,1.36860228401446,-0.225770985659268,1.51647060442954,-1.54875280423022,0.584613749636069,0.123854243844614,0.215941568743973,0.379639482759882,-0.502323453109302,-0.33320738366942,-1.01857538310709,-1.07179122647558,0.303528641404258,0.448209778629426,0.0530042267305041,0.922267467879738,2.05008468562714,-0.491031166056535,-2.30916887564081,1.00573852446226,-0.709200762582393,-0.688008616467358,1.0255713696967,-0.284773007051009,-1.22071771225454,0.18130347974915,-0.138891362439045,0.00576418589988693,0.38528040112633,-0.370660031792409,0.644376548518833,-0.220486561818751,0.331781963915697,1.09683901314935,0.435181490833803,-0.325931585531227,1.14880761845109,0.993503855962119,0.54839695950807,0.238731735111441,-0.627906076039371,1.36065244853001,-0.600259587147127,2.18733299301658,1.53261062618519,-0.235700359100477,-1.02642090030678,-0.710406563699301,0.25688370915653,-0.246691878462374,-0.347542599397733,-0.951618567265016,-0.0450277248089203,-0.784904469457076,-1.66794193658814,-0.380226520287762,0.918996609060766,-0.575346962608392,0.607964322225033,-1.61788270828916,-0.0555619655245394,0.519407203943462,0.301153362166714,0.105676194148943,-0.640706008305376,-0.849704346033582,-1.02412879060491,0.117646597100126,-0.947474614184802,-0.490557443700668,-0.256092192198247,1.84386200523221,-0.651949901695459,0.235386572284857,0.0779608495637108,-0.961856634130129,-0.0713080861235987,1.44455085842335,0.451504053079215,0.0412329219929399,-0.422496832339625,-2.05324722154052,1.13133721341418,-1.46064007092482,0.739947510877334,1.90910356921748,-1.4438931609718,0.701784335374711,-0.262197489402468,-1.57214415914549,-1.51466765378175,-1.60153617357459,-0.530906522170303,-1.4617555849959,0.687916772975828,2.10010894052567,-1.28703047603518,0.787738847475178,0.76904224100091,0.332202578950118,-1.00837660827701,-0.119452606630659,-0.280395335170247,0.56298953322048,-0.372438756103829,0.976973386685621,-0.374580857767014,1.05271146557933,-1.04917700666607,-1.26015524475811,3.2410399349424,-0.416857588160432,0.298227591540715,0.636569674033849,-0.483780625708744,0.516862044313609,0.368964527385086,-0.215380507641693,0.0652930335253153,-0.034067253738464,2.12845189901618,-0.741336096272828,-1.09599626707466,0.0377883991710788,0.310480749443137,0.436523478910183,-0.458365332711106,-1.06332613397119,1.26318517608949,-0.349650387953555,-0.865512862653374,-0.236279568941097,-0.197175894348552,1.10992028971364,0.0847372921971965,0.754053785184521,-0.499292017172261,0.214445309581601,-0.324685911490835,0.0945835281735714,-0.895363357977542,-1.31080153332797,1.99721338474797,0.600708823672418,-1.25127136162494,-0.611165916680421,-1.18548008459731)
t_bimodal <- c(0.19881034888372,-0.68758702356649,-2.26514505669635,-1.45680594076791,-2.41433994791886,-2.47624689461558,-2.78860283785024,-2.59461726745951,-0.34909253266331,-2.05402812508544,-1.88075476357242,-1.75631257040091,-0.767524121514662,-2.51606383094478,-2.99250715039204,-0.32430306759681,-2.44116321690529,-2.72306596993987,-3.23627311888329,-3.2847157223178,-2.57397347929799,-1.38201418283347,-0.890151861070282,-1.29241164616441,-2.36365729709525,-1.9402500626154,-2.70459646368007,-2.71721816157401,-1.11534950102308,-3.01559257860354,-0.0447060345075361,-2.09031959396585,-1.78546117337078,-2.73852770473957,-2.57438868976327,-3.31701613230524,-2.18292538837273,-1.58101759507554,-1.67569565583862,-2.78153648705475,-2.788621970854,-2.50219871834286,-0.503939330153649,-3.13730362066574,-2.1790515943802,-0.0976381783210729,-2.10097488532881,-3.35984070382139,-2.66476943527406,-1.51454002109512,-2.37560287166977,-2.56187636354978,-2.34391723412846,-1.90950335286078,-0.401491228854174,-2.08856511213888,-0.919200503848483,-1.36924588434943,-2.11363989550614,-3.5329020028906,-2.52111731755252,-2.48987045313847,-1.95284556723847,-0.699801322333179,0.293078973831094,-0.452418941016231,-2.13315096432894,-3.75652739555764,-2.38877986407174,-1.91079277692671,-1.15498699593256,-1.03747203151573,-1.31569057058354,-3.39527434979947,-1.15035695436664,-2.44655721642722,-1.82519729983874,-1.92544882282627,-1.57183323502949,-1.97532501717386,-3.66747509758566,-1.26350403522656,-1.61397343165032,-2.26565162527822,-1.88185548895332,-1.86596135463154,-1.778980531439,-0.359153834022514,-2.21905037893348,-1.83193461611534,-0.831616126930907,-0.945818976623081,-0.854736889619643,-2.57746800105956,0.00248273029282942,-1.93329912906982,-0.133148155293138,-3.35090268603071,-1.97901641364576,-0.750085429030784,1.2847578127772,1.24731103178226,1.06146129639311,0.947486720661263,1.5628404668196,2.33117917295898,-0.01421049792072,2.21198043337229,3.23667504641657,4.03757401824044,3.30117599220059,2.75677476379596,0.27326960088567,1.39849329199322,1.64795354341738,2.70352390275689,1.89432866599623,0.741351371939828,3.68443570809411,2.91139129179596,2.23743027249103,3.21810861032581,0.661225712765028,2.6608202977898,1.47708762368658,2.68374552185071,1.93917804533993,2.63296071303145,3.33551761505939,2.0072900903169,3.01755863695209,0.811565964852021,1.27839555956398,3.51921771138818,2.37738797302393,-0.0522228204337298,0.635962547917625,1.79921898441088,2.86577940433449,1.89811674428478,2.62418747202065,2.95900537778783,3.67105482886294,2.05601673327496,1.9480180938191,0.24676264085773,2.09932759408783,1.42814994210444,1.02599041719591,1.82009376895246,3.01494317274366,0.00725151131141755,1.57272071279457,2.11663728358271,1.10679242994505,2.33390294249923,2.41142992061573,1.96696384072401,-0.465898193760027,4.57145814586664,1.7947007425318,2.65119328158767,2.27376649103655,3.02467323481835,2.81765944637409,1.79020682877149,2.37816777220851,1.05459116887611,2.85692301089932,1.53896166111565,4.41677335378821,0.348951104311813,1.53601275703399,2.82537986275924,2.51013254687866,1.410518961485,1.00321925779248,2.1444757047107,1.98569258683309,0.20971876273594,2.03455106713385,2.19023031569246,2.17472639698184,0.944982957397319,2.47613327830263,3.37857013695924,2.45623640317981,0.864411529625657,1.5643545303081,2.34610361955361,1.35295436868173,-0.157646335015277,2.88425082002821,1.17052238837548,1.42643972923172,3.50390060900454,1.22585507039459,2.8457315401893,0.739317121181225,1.64545759692602)
t_uniform <- c(-0.117272665724158,-0.536618106998503,-1.51491178292781,-1.81202527787536,-0.948814783245325,1.87456467188895,-0.0460180705413222,-0.0887118810787797,0.995171524584293,0.670560925267637,-1.80233761295676,0.780420977622271,-0.546953733079135,1.53653442952782,1.10118891857564,-1.44318543467671,-0.819962915033102,-1.49566885828972,0.359606495127082,0.246702435426414,0.754884480498731,-0.754916763864458,0.422347365878522,1.96413727570325,0.972819660790265,-1.69657147862017,-0.195324374362826,-1.78585226181895,-0.641777943819761,0.935808595269918,-1.98357223812491,1.08763792924583,-0.148099155165255,0.883361400105059,0.666022868826985,0.288294882513583,0.815251800231636,0.628884241916239,-0.842591419816017,-1.61104217730463,1.84968528430909,0.945336116477847,0.450893874280155,-1.52028447668999,0.201036185957491,-0.948974888771772,1.5934433247894,-1.96328021679074,-1.05506025627255,-1.47982161864638,-0.695067413151264,0.90559555310756,1.96698094159365,0.86053411103785,0.0177592439576983,-0.255809312686324,1.79530100245029,-1.51927404943854,-1.69941929355264,1.55608597118407,-0.302191619761288,-1.8305572848767,0.5897685745731,-0.125523350201547,0.471704493276775,-0.916738269850612,-1.3708188533783,-1.54298291914165,0.0307314526289701,0.192129150032997,-1.43741524219513,-1.32092379964888,1.0479412926361,0.1095797624439,1.44395743031055,0.69421995151788,-1.94783588126302,0.77279560547322,1.56685492862016,0.52740070130676,-1.57082138024271,1.684260815382,0.701449524611235,-1.40562514960766,0.981367369182408,1.77039544750005,-0.316866497509181,-0.809107687324286,-0.962293319404125,-1.10847473237664,0.262617373839021,1.02660053130239,0.678414183668792,0.186091177165508,1.24585463106632,1.03666720818728,-1.9197261352092,-0.476385199464858,-1.79647954553366,1.19161174912006,1.69479685276747,0.170393481850624,1.40945840068161,0.33425145316869,0.673294574022293,0.0452583860605955,1.05100235715508,1.61344915162772,1.28189804870635,-1.71427260152996,-1.98441462777555,-1.79120553657413,1.46624071896076,0.304980675689876,-0.744629711844027,1.83786313515157,0.364775028079748,0.125637343153358,-0.464253313839436,-0.721787082031369,1.23354502115399,-1.83232200611383,-0.545029221102595,1.4263878678903,0.791786313988268,0.737945891916752,-0.607939789071679,0.218727317638695,-1.45102552976459,1.13972622528672,1.54745028633624,-1.18361646682024,1.08249184582382,0.385451843030751,1.83067896962166,-1.36524640955031,0.103897096589208,1.49260547012091,1.47882428113371,-1.90524542704225,1.90355877391994,-0.0391032071784139,-0.443318707868457,-0.329780160449445,-1.62829671800137,-1.35276315920055,-0.378334019333124,-0.632742223329842,-0.33897018712014,-0.783790123648942,0.241122149862349,-1.37650339771062,1.82631905656308,-1.82413349859416,-0.511369029060006,1.85046136565506,0.581710062921047,-1.75494954269379,-0.360216286033392,-0.296379463747144,0.0326323388144374,-0.201600356958807,0.493045534007251,-1.44008827582002,1.63178560324013,0.277773099020123,0.193122295662761,-1.53268950991333,1.04811332933605,-0.0865222131833434,1.12787565868348,-1.8158938055858,1.27937867119908,-0.922366210259497,-0.868598341941833,0.572886237874627,1.79247535113245,-1.97200618218631,-0.593529311940074,-0.323817100375891,-0.168492811731994,0.846770317293704,1.67939223069698,0.508442802354693,1.60872711334378,1.02931663673371,-1.44856653735042,-1.38698048796505,-1.2347512524575,-0.267259437590837,-1.65112279262394,-1.10487999580801,0.28859471809119,-0.399323286488652,0.261861522682011,1.31849551480263,0.568455277942121,-0.43400499317795,0.838319418951869,-1.56470370758325)
test_data <- data.frame(norm=t_norm, bimodal=t_bimodal, uniform=t_uniform)
small_data <- data.frame(x=c(-3.5, 0, 0.5, 6))

# Calculating density: d
d <- density(test_data$norm)

# Use which.max() to calculate mode
mode <- d$x[which.max(d$y)]

# Finish the ggplot call
ggplot(test_data, aes(x = norm)) +
  geom_rug() +
  geom_density() +
  geom_vline(xintercept = mode, col = "red")

# Arguments you'll need later on
fun_args <- list(mean = mean(test_data$norm), sd = sd(test_data$norm))

# Finish the ggplot
ggplot(test_data, aes(x = norm)) + 
  geom_histogram(aes(y=..density..)) + 
  geom_density(col="red") + 
  stat_function(fun=dnorm, args=fun_args, col="blue")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Get the bandwith
get_bw <- density(small_data$x)$bw

# Basic plotting object
p <- ggplot(small_data, aes(x = x)) +
  geom_rug() +
  coord_cartesian(ylim = c(0,0.5))

# Create three plots
p + geom_density()

p + geom_density(adjust=0.25)

# p + geom_density(bw = 0.25*get_bw)  ** does not work with my version of R/ggplot2 **

# Create two plots
p + geom_density(kernel="r")

p + geom_density(kernel="e")

There are multiple options for handling multiple groups (levels within a factor) or variables:

• One option for box plots is to use geom_boxplot(varwidth=TRUE)
  
• The violin plot is also nice for this - geom_violin(aes(weight=), col=NA) # to use default colors and weight by a given variable
  
• The 2D density plot can help show bimodal data - geom_density_2d()
  • Alternately, can use library(viridis); data(faithful)
    
  • ggplot(faithful, aes(x=waiting, y=eruptions)) + stat_density_2d(geom = “tile”, aes(fill = ..density..), contour=FALSE) + scale_fill_viridis()

Example code includes:

# Create the data
norm0 <- c(-0.56, -0.23, 1.56, 0.07, 0.13, 1.72, 0.46, -1.27, -0.69, -0.45, 1.22, 0.36, 0.4, 0.11, -0.56, 1.79, 0.5, -1.97, 0.7, -0.47, -1.07, -0.22, -1.03, -0.73, -0.63, -1.69, 0.84, 0.15, -1.14, 1.25, 0.43, -0.3, 0.9, 0.88, 0.82, 0.69, 0.55, -0.06, -0.31, -0.38, -0.69, -0.21, -1.27, 2.17, 1.21, -1.12, -0.4, -0.47, 0.78, -0.08, 0.25, -0.03, -0.04, 1.37, -0.23, 1.52, -1.55, 0.58, 0.12, 0.22, 0.38, -0.5, -0.33, -1.02, -1.07, 0.3, 0.45, 0.05, 0.92, 2.05, -0.49, -2.31, 1.01, -0.71, -0.69, 1.03, -0.28, -1.22, 0.18, -0.14, 0.01, 0.39, -0.37, 0.64, -0.22, 0.33, 1.1, 0.44, -0.33, 1.15, 0.99, 0.55, 0.24, -0.63, 1.36, -0.6, 2.19, 1.53, -0.24, -1.03, -0.71, 0.26, -0.25, -0.35, -0.95, -0.05, -0.78, -1.67, -0.38, 0.92, -0.58, 0.61, -1.62, -0.06, 0.52, 0.3, 0.11, -0.64, -0.85, -1.02, 0.12, -0.95, -0.49, -0.26, 1.84, -0.65, 0.24, 0.08, -0.96, -0.07, 1.44, 0.45, 0.04, -0.42, -2.05, 1.13, -1.46, 0.74, 1.91, -1.44, 0.7, -0.26, -1.57, -1.51, -1.6, -0.53, -1.46, 0.69, 2.1, -1.29, 0.79, 0.77, 0.33, -1.01, -0.12, -0.28, 0.56, -0.37, 0.98, -0.37, 1.05, -1.05, -1.26, 3.24, -0.42, 0.3, 0.64, -0.48, 0.52, 0.37, -0.22, 0.07, -0.03, 2.13, -0.74, -1.1, 0.04, 0.31, 0.44, -0.46, -1.06, 1.26, -0.35, -0.87, -0.24, -0.2, 1.11, 0.08, 0.75, -0.5, 0.21, -0.32, 0.09, -0.9, -1.31, 2, 0.6, -1.25, -0.61, -1.19)
bimodal0 <- c(0.2, -0.69, -2.27, -1.46, -2.41, -2.48, -2.79, -2.59, -0.35, -2.05, -1.88, -1.76, -0.77, -2.52, -2.99, -0.32, -2.44, -2.72, -3.24, -3.28, -2.57, -1.38, -0.89, -1.29, -2.36, -1.94, -2.7, -2.72, -1.12, -3.02, -0.04, -2.09, -1.79, -2.74, -2.57, -3.32, -2.18, -1.58, -1.68, -2.78, -2.79, -2.5, -0.5, -3.14, -2.18, -0.1, -2.1, -3.36, -2.66, -1.51, -2.38, -2.56, -2.34, -1.91, -0.4, -2.09, -0.92, -1.37, -2.11, -3.53, -2.52, -2.49, -1.95, -0.7, 0.29, -0.45, -2.13, -3.76, -2.39, -1.91, -1.15, -1.04, -1.32, -3.4, -1.15, -2.45, -1.83, -1.93, -1.57, -1.98, -3.67, -1.26, -1.61, -2.27, -1.88, -1.87, -1.78, -0.36, -2.22, -1.83, -0.83, -0.95, -0.85, -2.58, 0, -1.93, -0.13, -3.35, -1.98, -0.75, 1.28, 1.25, 1.06, 0.95, 1.56, 2.33, -0.01, 2.21, 3.24, 4.04, 3.3, 2.76, 0.27, 1.4, 1.65, 2.7, 1.89, 0.74, 3.68, 2.91, 2.24, 3.22, 0.66, 2.66, 1.48, 2.68, 1.94, 2.63, 3.34, 2.01, 3.02, 0.81, 1.28, 3.52, 2.38, -0.05, 0.64, 1.8, 2.87, 1.9, 2.62, 2.96, 3.67, 2.06, 1.95, 0.25, 2.1, 1.43, 1.03, 1.82, 3.01, 0.01, 1.57, 2.12, 1.11, 2.33, 2.41, 1.97, -0.47, 4.57, 1.79, 2.65, 2.27, 3.02, 2.82, 1.79, 2.38, 1.05, 2.86, 1.54, 4.42, 0.35, 1.54, 2.83, 2.51, 1.41, 1, 2.14, 1.99, 0.21, 2.03, 2.19, 2.17, 0.94, 2.48, 3.38, 2.46, 0.86, 1.56, 2.35, 1.35, -0.16, 2.88, 1.17, 1.43, 3.5, 1.23, 2.85, 0.74, 1.65)
value2 <- c(-0.56, -0.23, 1.559, 0.071, 0.129, 1.715, 0.461, -1.265, -0.687, -0.446, 1.224, 0.36, 0.401, 0.111, -0.556, 1.787, 0.498, -1.967, 0.701, -0.473, -1.068, -0.218, -1.026, -0.729, -0.625, -1.687, 0.838, 0.153, -1.138, 1.254, 0.426, -0.295, 0.895, 0.878, 0.822, 0.689, 0.554, -0.062, -0.306, -0.38, -0.695, -0.208, -1.265, 2.169, 1.208, -1.123, -0.403, -0.467, 0.78, -0.083, 0.253, -0.029, -0.043, 1.369, -0.226, 1.516, -1.549, 0.585, 0.124, 0.216, 0.38, -0.502, -0.333, -1.019, -1.072, 0.304, 0.448, 0.053, 0.922, 2.05, -0.491, -2.309, 1.006, -0.709, -0.688, 1.026, -0.285, -1.221, 0.181, -0.139, 0.006, 0.385, -0.371, 0.644, -0.22, 0.332, 1.097, 0.435, -0.326, 1.149, 0.994, 0.548, 0.239, -0.628, 1.361, -0.6, 2.187, 1.533, -0.236, -1.026, -0.71, 0.257, -0.247, -0.348, -0.952, -0.045, -0.785, -1.668, -0.38, 0.919, -0.575, 0.608, -1.618, -0.056, 0.519, 0.301, 0.106, -0.641, -0.85, -1.024, 0.118, -0.947, -0.491, -0.256, 1.844, -0.652, 0.235, 0.078, -0.962, -0.071, 1.445, 0.452, 0.041, -0.422, -2.053, 1.131, -1.461, 0.74, 1.909, -1.444, 0.702, -0.262, -1.572, -1.515, -1.602, -0.531, -1.462, 0.688, 2.1, -1.287, 0.788, 0.769, 0.332, -1.008, -0.119, -0.28, 0.563, -0.372, 0.977, -0.375, 1.053, -1.049, -1.26, 3.241, -0.417, 0.298, 0.637, -0.484, 0.517, 0.369, -0.215, 0.065, -0.034, 2.128, -0.741, -1.096, 0.038, 0.31, 0.437, -0.458, -1.063, 1.263, -0.35, -0.866, -0.236, -0.197, 1.11, 0.085, 0.754, -0.499, 0.214, -0.325, 0.095, -0.895, -1.311, 1.997, 0.601, -1.251, -0.611, -1.185, 0.199, -0.688, -2.265, -1.457, -2.414, -2.476, -2.789, -2.595, -0.349, -2.054, -1.881, -1.756, -0.768, -2.516, -2.993, -0.324, -2.441, -2.723, -3.236, -3.285, -2.574, -1.382, -0.89, -1.292, -2.364, -1.94, -2.705, -2.717, -1.115, -3.016, -0.045, -2.09, -1.785, -2.739, -2.574, -3.317, -2.183, -1.581, -1.676, -2.782, -2.789, -2.502, -0.504, -3.137, -2.179, -0.098, -2.101, -3.36, -2.665, -1.515, -2.376, -2.562, -2.344, -1.91, -0.401, -2.089, -0.919, -1.369, -2.114, -3.533, -2.521, -2.49, -1.953, -0.7, 0.293, -0.452, -2.133, -3.757, -2.389, -1.911, -1.155, -1.037, -1.316, -3.395, -1.15, -2.447, -1.825, -1.925, -1.572, -1.975, -3.667, -1.264, -1.614, -2.266, -1.882, -1.866, -1.779, -0.359, -2.219, -1.832, -0.832, -0.946, -0.855, -2.577, 0.002, -1.933, -0.133, -3.351, -1.979, -0.75, 1.285, 1.247, 1.061, 0.947, 1.563, 2.331, -0.014, 2.212, 3.237, 4.038, 3.301, 2.757, 0.273, 1.398, 1.648, 2.704, 1.894, 0.741, 3.684, 2.911, 2.237, 3.218, 0.661, 2.661, 1.477, 2.684, 1.939, 2.633, 3.336, 2.007, 3.018, 0.812, 1.278, 3.519, 2.377, -0.052, 0.636, 1.799, 2.866, 1.898, 2.624, 2.959, 3.671, 2.056, 1.948, 0.247, 2.099, 1.428, 1.026, 1.82, 3.015, 0.007, 1.573, 2.117, 1.107, 2.334, 2.411, 1.967, -0.466, 4.571, 1.795, 2.651, 2.274, 3.025, 2.818, 1.79, 2.378, 1.055, 2.857, 1.539, 4.417, 0.349, 1.536, 2.825, 2.51, 1.411, 1.003, 2.144, 1.986, 0.21, 2.035, 2.19, 2.175, 0.945, 2.476, 3.379, 2.456, 0.864, 1.564, 2.346, 1.353, -0.158, 2.884, 1.171, 1.426, 3.504, 1.226, 2.846, 0.739, 1.645)
dist2 <- c(1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2)
test_data <- data.frame(norm=norm0, bimodal=bimodal0)
test_data2 <- data.frame(dist=factor(dist2, labels=c("norm", "bimodal")), value=value2)

str(test_data)

## 'data.frame':    200 obs. of  2 variables:
##  $ norm   : num  -0.56 -0.23 1.56 0.07 0.13 1.72 0.46 -1.27 -0.69 -0.45 ...
##  $ bimodal: num  0.2 -0.69 -2.27 -1.46 -2.41 -2.48 -2.79 -2.59 -0.35 -2.05 ...

str(test_data2)

## 'data.frame':    400 obs. of  2 variables:
##  $ dist : Factor w/ 2 levels "norm","bimodal": 1 1 1 1 1 1 1 1 1 1 ...
##  $ value: num  -0.56 -0.23 1.559 0.071 0.129 ...

# Plot with test_data
ggplot(test_data, aes(x = norm)) +
  geom_rug() + 
  geom_density()

# Plot two distributions with test_data2
ggplot(test_data2, aes(x = value, fill = dist, col = dist)) +
  geom_rug(alpha=0.6) + 
  geom_density(alpha=0.6)

data(msleep); mammals <- msleep[!is.na(msleep$vore), c("vore", "sleep_total")]

# Individual densities
ggplot(mammals[mammals$vore == "insecti", ], aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3))

# With faceting
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3)) +
  facet_wrap(~ vore, nrow=2)

# Note that by default, the x ranges fill the scale
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3))

# Trim each density plot individually
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35, trim=TRUE) +
  scale_x_continuous(limits=c(0,24)) +
  coord_cartesian(ylim = c(0, 0.3))

# Density plot from before
ggplot(mammals, aes(x = sleep_total, fill = vore)) +
  geom_density(col = NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3))

# Finish the dplyr command
library(dplyr)
mammals2 <- mammals %>%
  group_by(vore) %>%
  mutate(n=n()/nrow(mammals))

# Density plot, weighted
ggplot(mammals2, aes(x = sleep_total, fill = vore)) +
  geom_density(aes(weight = n), col=NA, alpha = 0.35) +
  scale_x_continuous(limits = c(0, 24)) +
  coord_cartesian(ylim = c(0, 0.3))

## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density

## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density

## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density

## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density

# Violin plot
ggplot(mammals, aes(x = vore, y = sleep_total, fill = vore)) +
  geom_violin()

# Violin plot, weighted
ggplot(mammals2, aes(x = vore, y = sleep_total, fill = vore)) +
  geom_violin(aes(weight = n), col=NA)

## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density

## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density

## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density

## Warning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel
## = kernel, : sum(weights) != 1 -- will not get true density

data(faithful)

# Base layers
p <- ggplot(faithful, aes(x = waiting, y = eruptions)) +
  scale_y_continuous(limits = c(1, 5.5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(40, 100), expand = c(0, 0)) +
  coord_fixed(60 / 4.5)

# Use geom_density_2d()
p + geom_density_2d()

# Use stat_density_2d()
p + stat_density_2d(h=c(5, 0.5), aes(col=..level..))

# Load in the viridis package
library(viridis)

## Warning: package 'viridis' was built under R version 3.2.5

# Load in the viridis package
library(viridis)

# Add viridis color scale
ggplot(faithful, aes(x = waiting, y = eruptions)) +
  scale_y_continuous(limits = c(1, 5.5), expand = c(0,0)) +
  scale_x_continuous(limits = c(40, 100), expand = c(0,0)) +
  coord_fixed(60/4.5) +
  stat_density_2d(geom = "tile", aes(fill = ..density..), h=c(5,.5), contour = FALSE) +
  scale_fill_viridis()

Defining largeness of a dataset - # observations, # variables, etc. For many observations (e.g., “diamonds” dataset) - can adjust point size, alpha blending, 2-d contours, etc.:

• Alternately, can bin the values in a 2D manner - geom_bin2d(bins=)
  
• Can also use geom_hex(bins=) for hex_binning - uses hexagons rather than squares

For many variables, can use pre-processing such as PCA:

• Can plot using facets for factor variables, but this is only good for small numbers of facets
  
• SPLOM - scatterplot matrix, e.g., pairs()
  
• library(PerformanceAnalytics); chart.Correlation(iris[-5])
  
• library(Ggally); ggpairs(mtcars)

Ternary plot - triangle plot where all the components add to 100% (e.g., soil composition):

• Soil has a sand, silt, and clay component
  
• Each variable of interest forms a side of a triangle

Network plots (e.g., blood type donors):

• Which blood types can be donors for other types?
  
• The network is called by geom_net()

Diagnostic plots:

• Can plot OLS models and qualities of fit
  
• Calling plot() on an lm result provides four plots
  
• Can use car::qqplot() or car::influencePlot()

Example code includes:

# pairs
data(iris)
pairs(iris[, 1:4])

# chart.Correlation
library(PerformanceAnalytics)

## Warning: package 'PerformanceAnalytics' was built under R version 3.2.5

## Loading required package: xts

## Warning: package 'xts' was built under R version 3.2.5

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

## 
## Attaching package: 'xts'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

## 
## Attaching package: 'PerformanceAnalytics'

## The following object is masked from 'package:graphics':
## 
##     legend

chart.Correlation(iris[, 1:4])

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter

## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter

## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

## Warning in axis(side = side, at = at, labels = labels, ...): "method" is
## not a graphical parameter

## Warning in plot.xy(xy.coords(x, y), type = type, ...): "method" is not a
## graphical parameter

## Warning in plot.window(...): "method" is not a graphical parameter

## Warning in plot.xy(xy, type, ...): "method" is not a graphical parameter

## Warning in title(...): "method" is not a graphical parameter

# ggpairs
library(GGally)
data(mtcars); mtcars_fact <- mtcars; mtcars_fact$cyl <- as.factor(mtcars_fact$cyl)
ggpairs(mtcars_fact[, 1:3])

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

library(ggplot2)
library(reshape2)

cor_list <- function(x) {
  L <- M <- cor(x)
  
  M[lower.tri(M, diag = TRUE)] <- NA
  M <- melt(M)
  names(M)[3] <- "points"
  
  L[upper.tri(L, diag = TRUE)] <- NA
  L <- melt(L)
  names(L)[3] <- "labels"
  
  merge(M, L)
}

# Calculate xx with cor_list
library(dplyr)
xx <- iris %>%
  group_by(Species) %>%
  do(cor_list(.[1:4])) 

# Finish the plot
ggplot(xx, aes(x = Var1, y = Var2)) +
  geom_point(aes(col = points, size = abs(points)), shape = 16) +
  geom_text(aes(col = labels,  size = labels, label = round(labels, 2))) +
  scale_size(range = c(0, 6)) +
  scale_color_gradient("r", limits = c(-1, 1)) +
  scale_y_discrete("", limits = rev(levels(xx$Var1))) +
  scale_x_discrete("") +
  guides(size = FALSE) +
  geom_abline(slope = -1, intercept = nlevels(xx$Var1) + 1) +
  coord_fixed() +
  facet_grid(. ~ Species) +
  theme(axis.text.y = element_text(angle = 45, hjust = 1),
        axis.text.x = element_text(angle = 45, hjust = 1),
        strip.background = element_blank())

## Warning: Removed 30 rows containing missing values (geom_point).

## Warning: Removed 30 rows containing missing values (geom_text).

# Explore africa
library(GSIF)

## Warning: package 'GSIF' was built under R version 3.2.5

## GSIF version 0.5-3 (2016-07-18)

## URL: http://gsif.r-forge.r-project.org/

data(afsp)
str(afsp)

## List of 2
##  $ sites   :'data.frame':    26270 obs. of  9 variables:
##   ..$ SOURCEID: Factor w/ 26270 levels "100902","100903",..: 5606 5607 5608 5609 5610 5611 5604 5605 5681 5682 ...
##   ..$ SOURCEDB: Factor w/ 7 levels "Af_LDSF","Af_soilspec",..: 2 2 2 2 2 2 2 2 2 2 ...
##   ..$ LONWGS84: num [1:26270] 36.1 36.1 36.1 36.1 36.1 ...
##   ..$ LATWGS84: num [1:26270] -6.93 -6.93 -6.93 -6.93 -6.93 ...
##   ..$ TIMESTRR: Date[1:26270], format: "2012-10-02" ...
##   ..$ TAXGWRB : Factor w/ 40 levels "AC","AL","Alisol",..: NA NA NA NA NA NA NA NA NA NA ...
##   ..$ TAXNUSDA: Factor w/ 960 levels "\"Plinthic\" Udoxic Dystropept",..: NA NA NA NA NA NA NA NA NA NA ...
##   ..$ BDRICM  : num [1:26270] NA NA NA NA NA NA NA NA NA NA ...
##   ..$ DRAINFAO: Factor w/ 7 levels "E","I","M","P",..: NA NA NA NA NA NA NA NA NA NA ...
##  $ horizons:'data.frame':    87693 obs. of  14 variables:
##   ..$ SOURCEID: Factor w/ 26270 levels "100902","100903",..: 5606 5606 5607 5607 5607 5607 5608 5608 5609 5609 ...
##   ..$ UHDICM  : num [1:87693] 0 20 0 20 0 20 0 20 0 20 ...
##   ..$ LHDICM  : num [1:87693] 20 50 20 50 20 50 20 50 20 50 ...
##   ..$ MCOMNS  : Factor w/ 289 levels "10BG4/1","10R2.5/1",..: NA NA NA NA NA NA NA NA NA NA ...
##   ..$ ORCDRC  : num [1:87693] 4.36 3.69 3.67 4 5.25 4.57 6.02 3.6 3.33 5.33 ...
##   ..$ PHIHOX  : num [1:87693] 7.23 7.3 7.06 6.95 7.21 7.25 7.35 7.44 7.26 6.94 ...
##   ..$ SNDPPT  : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
##   ..$ SLTPPT  : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
##   ..$ CLYPPT  : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
##   ..$ CRFVOL  : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
##   ..$ BLD     : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
##   ..$ CEC     : num [1:87693] NA NA NA NA NA NA NA NA NA NA ...
##   ..$ NTO     : num [1:87693] 0.381 0.334 0.318 0.366 0.439 0.404 0.512 0.334 0.329 0.482 ...
##   ..$ EMGX    : num [1:87693] 2.66 2.95 2.73 3.84 2.36 3.16 3.59 4.37 3.71 2.76 ...

africa <- afsp$horizons[, c("SNDPPT", "SLTPPT", "CLYPPT")]
africa <- africa[complete.cases(africa), ]
africa <- africa / rowSums(africa)
africa <- africa %>% rename(Sand=SNDPPT, Silt=SLTPPT, Clay=CLYPPT)
str(africa)

## 'data.frame':    53830 obs. of  3 variables:
##  $ Sand: num  0.845 0.838 0.694 0.661 0.647 ...
##  $ Silt: num  0.0326 0.0313 0.0387 0.0782 0.1176 ...
##  $ Clay: num  0.122 0.131 0.268 0.261 0.235 ...

# Sample the dataset
africa_sample <- africa[sample(1:nrow(africa), size = 50),]

# Add an ID column from the row.names
africa_sample$ID <- row.names(africa_sample)

# Gather africa_sample
library(tidyr)
africa_sample_tidy <- gather(africa_sample, key, value, -ID)

# Finish the ggplot command
ggplot(africa_sample_tidy, aes(x = factor(ID), y = value, fill = key)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_x_discrete(expand = c(0,0)) +
  scale_y_continuous(expand = c(0,0)) +
  labs(x = "Location", y = "Composition", fill = "Component") +
  theme_minimal()

# Load ggtern
library(ggtern)

## Warning: package 'ggtern' was built under R version 3.2.5

## --
## Consider donating at: http://ggtern.com
## Even small amounts (say $10-50) are very much appreciated!
## Remember to cite, run citation(package = 'ggtern') for further info.
## --

## 
## Attaching package: 'ggtern'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, aes, annotate, calc_element, ggplot, ggplot_build,
##     ggplot_gtable, ggplotGrob, ggsave, layer_data, theme,
##     theme_bw, theme_classic, theme_dark, theme_gray, theme_light,
##     theme_linedraw, theme_minimal, theme_void

# Build ternary plot
# ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
#   geom_point(shape=16, alpha=0.2)


# ggtern and ggplot2 are loaded

# Plot 1
# ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
#   geom_density_tern()

# Plot 2
# ggtern(africa, aes(x = Sand, y = Silt, z = Clay)) +
#  stat_density_tern(geom="polygon", aes(fill = ..level.., alpha = ..level..), 
#                    guides(fill = guide_legend(show = FALSE))
#                    )


# Load geomnet
library(geomnet)

## Warning: package 'geomnet' was built under R version 3.2.5

# Examine structure of madmen
str(geomnet::madmen)

## List of 2
##  $ edges   :'data.frame':    39 obs. of  2 variables:
##   ..$ Name1: Factor w/ 9 levels "Betty Draper",..: 1 1 2 2 2 2 2 2 2 2 ...
##   ..$ Name2: Factor w/ 39 levels "Abe Drexler",..: 15 31 2 4 5 6 8 9 11 21 ...
##  $ vertices:'data.frame':    45 obs. of  2 variables:
##   ..$ label : Factor w/ 45 levels "Abe Drexler",..: 5 9 16 23 26 32 33 38 39 17 ...
##   ..$ Gender: Factor w/ 2 levels "female","male": 1 2 2 1 2 1 2 2 2 2 ...

# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
               by.x = "Name1", by.y="label",
               all = TRUE)

# Examin structure of mmnet
str(mmnet)

## 'data.frame':    75 obs. of  3 variables:
##  $ Name1 : Factor w/ 45 levels "Betty Draper",..: 1 1 2 2 2 2 2 2 2 2 ...
##  $ Name2 : Factor w/ 39 levels "Abe Drexler",..: 15 31 2 4 5 6 8 9 11 21 ...
##  $ Gender: Factor w/ 2 levels "female","male": 1 1 2 2 2 2 2 2 2 2 ...

# geomnet is pre-loaded

# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
               by.x = "Name1", by.y = "label",
               all = TRUE)

# Finish the ggplot command
# ggplot(data = mmnet, aes(from_id = Name1, to_id = Name2)) +
#   geom_net(aes(col=Gender), size=6, linewidth=1, label=TRUE, fontsize=3, labelcolour="black")

# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
               by.x = "Name1", by.y = "label",
               all = TRUE)

# Tweak the network plot
# ggplot(data = mmnet, aes(from_id = Name1, to_id = Name2)) +
#   geom_net(aes(col = Gender),
#            size = 6,
#            linewidth = 1,
#            label = TRUE,
#            fontsize = 3,
#            labelcolour = "black",
#            directed=TRUE) +
#   scale_color_manual(values = c("#FF69B4", "#0099ff")) +
#   xlim(c(-0.05, 1.05)) +
#   ggmap::theme_nothing(legend=TRUE) +
#   theme(legend.key=element_blank())


# Merge edges and vertices
mmnet <- merge(madmen$edges, madmen$vertices,
               by.x = "Name1", by.y = "label",
               all = TRUE)


# Create linear model: res
data(trees)
res <- lm(Volume ~ Girth, data = trees)

# Plot res
par(mfrow = c(2, 2))
plot(res)

par(mfrow = c(1, 1))

# Import ggfortify and use autoplot()
library(ggfortify)

## Warning: package 'ggfortify' was built under R version 3.2.5

autoplot(res, ncol=2)

# Inspect structure of Canada
library(vars); data(Canada)

## Warning: package 'vars' was built under R version 3.2.5

## Loading required package: MASS

## 
## Attaching package: 'MASS'

## The following object is masked _by_ '.GlobalEnv':
## 
##     mammals

## The following object is masked from 'package:dplyr':
## 
##     select

## Loading required package: strucchange

## Warning: package 'strucchange' was built under R version 3.2.4

## Loading required package: sandwich

## Loading required package: urca

## Warning: package 'urca' was built under R version 3.2.5

## Loading required package: lmtest

## Warning: package 'lmtest' was built under R version 3.2.5

str(Canada)

##  mts [1:84, 1:4] 930 930 930 931 933 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : NULL
##   ..$ : chr [1:4] "e" "prod" "rw" "U"
##  - attr(*, "tsp")= num [1:3] 1980 2001 4
##  - attr(*, "class")= chr [1:2] "mts" "ts"

# Call plot() on Canada
plot(Canada)

# Call autoplot() on Canada
# autoplot(Canada)


# ggfortify and eurodist are available

# Autoplot + ggplot2 tweaking
# autoplot(eurodist) + 
#   labs(x="", y="") +
#   coord_fixed() + 
#   theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

# Autoplot of MDS
# autoplot(cmdscale(eurodist, eig=TRUE), label=TRUE, label.size=3, size=0)


# perform clustering
iris_k <- kmeans(iris[-5], centers=3)

# autplot: coloring according to cluster
# autoplot(iris_k, data=iris, frame=TRUE)

# autoplot: coloring according to species
# autoplot(iris_k, data=iris, frame=TRUE, col="Species")

Choropleths are a series of polygons (or points or lines), and are useful when you have the shapes file and some underlying data. Maps (e.g., full US or by state or etc.) are a primary example - can put right in to ggplot2:

• The map is not always the best - Cleveland dot plot can be superior (can pick a more sensible order or better see central tendencies)
  
• Cleveland dot plots can also be easier for a sub-classification (such as region)

Cartographic maps are an alternative (topographical, photographic, etc.):

• The ggmap::get_map() is a good way to pull down maps
  
• The ggmap::geocode() is a good way to pull down the lat/lon for given landmarks (or the like)
  
• The ggmap::make_bbox() is a good way to set a boundary based on some points, as well as a desired buffer

Animations can be useful for dense, temporal data or as an exploratory tool:

• Motion Chart (Hans Rosling) showing UN data by geography over time
  
• The frame aesthetic allows for animation, and is used inside the aes() command
  
• The gg_animate() command is called on a ggplot() object, with interval=x to define how quickly to animate

Example code includes:

library(ggplot2)
library(ggmap)

## Warning: package 'ggmap' was built under R version 3.2.5

library(ggthemes)
library(maps)

## Warning: package 'maps' was built under R version 3.2.5

library(viridis)


# Basic map of the USA
usa <- ggplot2::map_data("usa")
str(usa)

## 'data.frame':    7243 obs. of  6 variables:
##  $ long     : num  -101 -101 -101 -101 -101 ...
##  $ lat      : num  29.7 29.7 29.7 29.6 29.6 ...
##  $ group    : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ order    : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ region   : chr  "main" "main" "main" "main" ...
##  $ subregion: chr  NA NA NA NA ...

ggplot(data=usa, aes(x=long, y=lat, group=group)) + 
    geom_polygon() + 
    coord_map()

# Add USA cities to the USA map (continental US only, cities with 250k+ population)
library(dplyr)
data(us.cities)
cities <- us.cities %>% 
    mutate(City=name, State=country.etc, Pop_est=pop) %>% 
    dplyr::select(City, State, Pop_est, lat, long) %>%
    filter(!(State %in% c("AK", "HI", "ma")) & Pop_est >= 250000)

ggplot(usa, aes(x = long, y = lat, group = group)) + 
    geom_polygon() + 
    geom_point(data=cities, aes(group=State, size=Pop_est, col="red"), shape=16, alpha=0.6) + 
    coord_map() + 
    theme_map()

# Arrange cities
cities_arr <- arrange(cities, Pop_est)

# Create US plot of cities, colored by the viridis theme
ggplot(usa, aes(x = long, y = lat, group = group)) + 
    geom_polygon(fill="grey90") + 
    geom_point(data=cities_arr, aes(group=State, col=Pop_est), shape=16, size=2, alpha=0.6) + 
    coord_map() + 
    theme_map() + 
    scale_color_viridis()

# Create a dataset of populations by state
st_data <- "california ; texas ; florida ; new york ; illinois ; pennsylvania ; ohio ; georgia ; north carolina ; michigan ; new jersey ; virginia ; washington ; arizona ; massachusetts ; indiana ; tennessee ; missouri ; maryland ; wisconsin ; minnesota ; colorado ; south carolina ; alabama ; louisiana ; kentucky ; oregon ; oklahoma ; connecticut ; puerto rico ; iowa ; utah ; mississippi ; arkansas ; kansas ; nevada ; new mexico ; nebraska ; west virginia ; idaho ; hawaii ; new hampshire ; maine ; rhode island ; montana ; delaware ; south dakota ; north dakota ; alaska ; district of columbia ; vermont ; wyoming"

pop_data <- "39144818 ; 27469114 ; 20271272 ; 19795791 ; 12859995 ; 12802503 ; 11613423 ; 10214860 ; 10042802 ; 9922576 ; 8958013 ; 8382993 ; 7170351 ; 6828065 ; 6794422 ; 6619680 ; 6600299 ; 6083672 ; 6006401 ; 5771337 ; 5489594 ; 5456574 ; 4896146 ; 4858979 ; 4670724 ; 4425092 ; 4028977 ; 3911338 ; 3590886 ; 3474182 ; 3123899 ; 2995919 ; 2992333 ; 2978204 ; 2911641 ; 2890845 ; 2085109 ; 1896190 ; 1844128 ; 1654930 ; 1431603 ; 1330608 ; 1329328 ; 1056298 ; 1032949 ; 945934 ; 858469 ; 756927 ; 738432 ; 672228 ; 626042 ; 586107"

pop <- data.frame(region=strsplit(st_data, split=" ; ")[[1]], 
                  Pop_est=as.numeric(strsplit(pop_data, split=" ; ")[[1]]), 
                  stringsAsFactors = FALSE
                  )


# Map the basic state data
state <- map_data("state")
ggplot(data=state, aes(x=long, y=lat, group=group, fill=region)) + 
    geom_polygon(col="white") + 
    coord_map()

# Map the states by population
state2 <- merge(state, pop, by="region")
ggplot(data=state2, aes(x=long, y=lat, group=group, fill=Pop_est)) + 
    geom_polygon(col="white") + 
    coord_map() + 
    theme_map()

# Import shape information: germany (commented out since files not available)
library(rgdal)

## Warning: package 'rgdal' was built under R version 3.2.5

## Loading required package: sp

## Warning: package 'sp' was built under R version 3.2.5

## rgdal: version: 1.2-3, (SVN revision 639)
##  Geospatial Data Abstraction Library extensions to R successfully loaded
##  Loaded GDAL runtime: GDAL 2.0.1, released 2015/09/15
##  Path to GDAL shared files: C:/Users/Dave/Documents/R/win-library/3.2/rgdal/gdal
##  GDAL does not use iconv for recoding strings.
##  Loaded PROJ.4 runtime: Rel. 4.9.1, 04 March 2015, [PJ_VERSION: 491]
##  Path to PROJ.4 shared files: C:/Users/Dave/Documents/R/win-library/3.2/rgdal/proj
##  Linking to sp version: 1.2-3

# germany <- readOGR(dsn="shapes", "DEU_adm1")
# bundes <- fortify(germany)
# 
# ggplot(data=bundes, aes(x=long, y=lat, group=group)) + 
#     geom_polygon(fill="blue", col="white") +
#     coord_map() + 
#     theme_nothing()
# 
# bundes$state <- factor(as.numeric(bundes$id))
# levels(bundes$state) <- germany$NAME_1
# 
# bundes_unemp <- merge(bundes, unemp, by="state")
# 
# ggplot(bundes_unemp, aes(x = long, y = lat, group = group, fill=unemployment)) + 
#     geom_polygon() + 
#     coord_map() + 
#     theme_map()


# Create the map of London
library(ggmap)
london_map_13 <- get_map("London, England", zoom=13)

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=London,+England&zoom=13&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=London,%20England&sensor=false

ggmap(london_map_13)

# Experiment with get_map() and use ggmap() to plot it!
# temp1 <- get_map("London, England", zoom=13, maptype="toner", source="stamen")
# ggmap(temp1)

temp2 <- get_map("London, England", zoom=13, maptype="hybrid")

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=London,+England&zoom=13&size=640x640&scale=2&maptype=hybrid&language=en-EN&sensor=false
## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=London,%20England&sensor=false

ggmap(temp2)

# Map some key sites in London
london_sites <- strsplit("Tower of London, London ; Buckingham Palace, London ; Tower Bridge, London ; Queen Elizabeth Olympic Park, London", " ; ")[[1]]

xx <- geocode(london_sites)

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Tower%20of%20London,%20London&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Buckingham%20Palace,%20London&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Tower%20Bridge,%20London&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Queen%20Elizabeth%20Olympic%20Park,%20London&sensor=false

xx$location <- sub(", London", "", london_sites)
london_ton_13 <- get_map(location = "London, England", zoom = 13,
                         source = "stamen", maptype = "toner")

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=London,+England&zoom=13&size=640x640&scale=2&maptype=terrain&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=London,%20England&sensor=false

## Map from URL : http://tile.stamen.com/toner/13/4091/2722.png

## Map from URL : http://tile.stamen.com/toner/13/4092/2722.png

## Map from URL : http://tile.stamen.com/toner/13/4093/2722.png

## Map from URL : http://tile.stamen.com/toner/13/4094/2722.png

## Map from URL : http://tile.stamen.com/toner/13/4091/2723.png

## Map from URL : http://tile.stamen.com/toner/13/4092/2723.png

## Map from URL : http://tile.stamen.com/toner/13/4093/2723.png

## Map from URL : http://tile.stamen.com/toner/13/4094/2723.png

## Map from URL : http://tile.stamen.com/toner/13/4091/2724.png

## Map from URL : http://tile.stamen.com/toner/13/4092/2724.png

## Map from URL : http://tile.stamen.com/toner/13/4093/2724.png

## Map from URL : http://tile.stamen.com/toner/13/4094/2724.png

## Map from URL : http://tile.stamen.com/toner/13/4091/2725.png

## Map from URL : http://tile.stamen.com/toner/13/4092/2725.png

## Map from URL : http://tile.stamen.com/toner/13/4093/2725.png

## Map from URL : http://tile.stamen.com/toner/13/4094/2725.png

# Add a geom_points layer
ggmap(london_ton_13) + 
    geom_point(data=xx, aes(col=location), size=6)

## Warning: Removed 1 rows containing missing values (geom_point).

# Expand to use the bounding box
xx <- geocode(london_sites)

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Tower%20of%20London,%20London&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Buckingham%20Palace,%20London&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Tower%20Bridge,%20London&sensor=false

## Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Queen%20Elizabeth%20Olympic%20Park,%20London&sensor=false

xx$location <- sub(", London", "", london_sites)
xx$location[4] <- "Queen Elizabeth\nOlympic Park"

# Create bounding box: bbox
bbox <- make_bbox(lon = xx$lon, lat = xx$lat, f = 0.3)
london_ton_13 <- get_map(bbox, zoom = 13,
                         source = "stamen", maptype = "toner"
                         )

## Map from URL : http://tile.stamen.com/toner/13/4091/2722.png

## Warning in file.remove(index[[url]]): cannot remove file
## 'a991dd39e80eba942f916d1a39eacba1.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4092/2722.png

## Warning in file.remove(index[[url]]): cannot remove file
## 'ca4d532407f75fb40f356d82e9f3e868.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4093/2722.png

## Warning in file.remove(index[[url]]): cannot remove file
## '2d23913e51259d39586aa2f72cf7262a.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4094/2722.png

## Warning in file.remove(index[[url]]): cannot remove file
## 'd95217086725a265fa36c032fbc90ad6.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4095/2722.png

## Map from URL : http://tile.stamen.com/toner/13/4096/2722.png

## Map from URL : http://tile.stamen.com/toner/13/4091/2723.png

## Warning in file.remove(index[[url]]): cannot remove file
## '34870d377d252bc5a4f1705aaec727a7.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4092/2723.png

## Warning in file.remove(index[[url]]): cannot remove file
## 'afecef47c6d1c2bb613ae817f73ef94e.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4093/2723.png

## Warning in file.remove(index[[url]]): cannot remove file
## 'f0de52ccfe7b807ab9e08f76c37ff7be.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4094/2723.png

## Warning in file.remove(index[[url]]): cannot remove file
## 'f7fedbdcd7d7ca7fa3714dc4a4b6b802.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4095/2723.png

## Map from URL : http://tile.stamen.com/toner/13/4096/2723.png

## Map from URL : http://tile.stamen.com/toner/13/4091/2724.png

## Warning in file.remove(index[[url]]): cannot remove file
## '8bebd0980b74870f3d7dc958d655198f.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4092/2724.png

## Warning in file.remove(index[[url]]): cannot remove file
## '245dcf359e94685490a556539f0ef26f.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4093/2724.png

## Warning in file.remove(index[[url]]): cannot remove file
## '92a8bede90fdcd1ba21c8c78c10f06b3.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4094/2724.png

## Warning in file.remove(index[[url]]): cannot remove file
## '9acc9d919db20a0d68fdef3dd4092cfe.rds', reason 'No such file or directory'

## Map from URL : http://tile.stamen.com/toner/13/4095/2724.png

## Map from URL : http://tile.stamen.com/toner/13/4096/2724.png

# Map from previous exercise
ggmap(london_ton_13) + 
    geom_point(data = xx, aes(col = location), size = 6)

# New map with labels
ggmap(london_ton_13) + 
    geom_label(data=xx, aes(label=location), size=4, fontface="bold", fill="grey90", col="#E41A1C")

# Get the map data of "Germany" and Plot map and polygon on top: (not displayed)
# germany_06 <- get_map("Germany", zoom=6)
# ggmap(germany_06) + 
#     geom_polygon(data=bundes, aes(x=long, y=lat, group=group), fill=NA, col="red") + 
#     coord_map()


# Animated Japan map (not shown)
# str(japan)
# 
# saveGIF({
# 
#   for (i in unique(japan$time)) {
# 
#     data <- japan[japan$time == i, ]
# 
#     p <- ggplot(data, aes(x = AGE, y = POP, fill = SEX, width = 1)) +
#       coord_flip() +
#       geom_bar(data = data[data$SEX == "Female",], stat = "identity") +
#       geom_bar(data = data[data$SEX == "Male",], stat = "identity") +
#       ggtitle(i)
# 
#     print(p)
# 
#   }
# 
# }, movie.name = "pyramid.gif", interval = 0.1)


# Animate the vocabularies by year
# library(gganimate)
# library(car)
# data(Vocab)
# p <- ggplot(Vocab, aes(x = education, y = vocabulary,
#                        color = year, group = year, 
#                        frame=year, cumulative=TRUE
#                        )
#             ) +
#     stat_smooth(method = "lm", se = FALSE, size = 3)
# 
# gg_animate(p, filename="vocab.gif", interval = 1.0)

The ggplot2 internals include 2 plotting systems in R (base and grid):

• base treats the graphics as static (cannot be changed after drawing - need to re-do from scratch)
  
• grid provides low-level graphics capabilities, meaning that it can be edited (create graphic outputs, and layer them)
  • Some of the grid capabilities include grid.rect(), grid.lines(), grid.text() and the like

There are many possible customizations to the grid process:

• gp=gpar() argument inside of each grid.xxx() which allows more control over things like fill
  
• name=“” argument which allows re-use of a specific grid-graphic system
  
• There are “viewports”, basically a fancy name for graphics displays
  • vp <- viewport() will create a new viewport that is not yet active
    
  • pushViewport(vp) will push this viewport on to the active device
    
  • The “viewports” can also be named for re-use
    
  • There is also a dataViewport() call which associates data to the viewport – vp_data <- dataViewport(mtcars\(wt, mtcars\)mpg)
    
• grid.edit() is the magic – can supply a named object and change it through this

The ggplot2 package is built using the grid() framework:

• There are both graphic outputs (as per above) and graphics objects (“grobs”)
  
• Underlying evey ggplot2 function, there will be a collection of grobs
  
• The function ggplotGrob() will print the grobs associated to “obj”
  
• Since these are at heart lists, can run g$grob[[8]], for example, to extract the eighth item from the grob list
  
• Can create new text using textGrob(label=“”, gp=gpar(fontsize=7, col=“gray25”))
  • Can then add this to an existing grob with gtable_add_grob()

Any time you create a ggplot (even if empty - ggplot()), you create an object:

• The object has 9 elements - name, layers, scales, mapping, theme, coordinates, facet, plot_env, labels
  
• The plot is actually displayed due to ggplot_build() – contains data, panel, plot
  
• The command gtab <- ggplot_gtable(p_build) is analogous to ggplotGrob for the grobs
  
• There is a gtab\(layout\)clip which tells whether each element is clipped

The gridExtra library allows for additional customization:

• Manage multiple plotting objects (avoid giant facets, defer plotting, arrange multiple plots, make a multi-page PDF, etc.)
  
• Can then treat the various layers of the plot as a list
  
• grid.arrange(multiple plots) and do.call()

You can write your own extensions in ggplot2 2.0 (e.g., the “bag plot” based on Tukey):

• The bag plot is a 2-D chart with a “bag” (contains 50% of the data) and a “loop” (factor enlargement of the bag) and “extremes” (outside the loop)
  
• Since the bag plot is not that popular, it is an example of how to create a ggplot extension

Case Study - weather, using NYC data and several Tukey-like graphs:

• data set “present” is the first half of the current year NYC temperatures
  
• data set “past” is historical data from the previous 20 years
  
• Goal is to create the historical data, 95% CI, and current year records; along with a custom legend

Case Study Part II - same idea, but increasingly efficient by way of several stat functions in ggplot2:

• Goal is to be more generalizable and flexible; no need to produce large chunks of code

Example code includes:

library(Hmisc)

## Loading required package: lattice

## Loading required package: survival

## 
## Attaching package: 'survival'

## The following object is masked from 'package:quantreg':
## 
##     untangle.specials

## The following object is masked from 'package:robustbase':
## 
##     heart

## Loading required package: Formula

## 
## Attaching package: 'Hmisc'

## The following object is masked from 'package:GSIF':
## 
##     describe

## The following objects are masked from 'package:dplyr':
## 
##     combine, src, summarize

## The following object is masked from 'package:quantreg':
## 
##     latex

## The following objects are masked from 'package:base':
## 
##     format.pval, round.POSIXt, trunc.POSIXt, units

library(grid)
library(gtable)

## Warning: package 'gtable' was built under R version 3.2.5

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following objects are masked from 'package:ggtern':
## 
##     arrangeGrob, grid.arrange

library(dplyr)
detach(package:dplyr)
library(plyr)

## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------
## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------
## -------------------------------------------------------------------------

## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)

## -------------------------------------------------------------------------

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:Hmisc':
## 
##     is.discrete, summarize

## The following object is masked from 'package:maps':
## 
##     ozone

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following objects are masked from 'package:Hmisc':
## 
##     combine, src, summarize

## The following object is masked from 'package:MASS':
## 
##     select

## The following objects are masked from 'package:xts':
## 
##     first, last

## The following object is masked from 'package:GGally':
## 
##     nasa

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(aplpack)

## Warning: package 'aplpack' was built under R version 3.2.5

## Loading required package: tcltk

# Draw rectangle in null viewport
grid.rect(gp=gpar(fill = "grey90"))

# Write text in null viewport
grid.text("null viewport")

# Draw a line
grid.lines(x=c(0, 0.75), y=c(0.25, 1), gp=gpar(lty=2, col="red"))


# Populate null viewport
grid.rect(gp = gpar(fill = "grey90"))
grid.text("null viewport")
grid.lines(x = c(0,0.75), y = c(0.25, 1),
           gp = gpar(lty = 2, col = "red"))

# Create new viewport: vp
vp <- viewport(x=0.5, y=0.5, width=0.5, height=0.5, just="center")

# Push vp
pushViewport(vp)

# Populate new viewport with rectangle
grid.rect(gp=gpar(fill="blue"))


# Create plot viewport: pvp
mar <- c(5, 4, 2, 2)
pvp <- plotViewport(mar)

# Push pvp
pushViewport(pvp)

# Add rectangle
grid.rect(gp=gpar(fill="grey80"))

# Create data viewport: dvp
dvp <- dataViewport(mtcars$wt, mtcars$mpg)

# Push dvp
pushViewport(dvp)

# Add two axes
grid.xaxis()
grid.yaxis()


# Work from before
pushViewport(plotViewport(c(5, 4, 2, 2)))
grid.rect(gp = gpar())
pushViewport(dataViewport(xData = mtcars$wt, yData = mtcars$mpg))
grid.xaxis()
grid.yaxis()

# Add text to x axis
grid.text(label="Weight", y=unit(-3, "lines"))

# Add text to y axis
grid.text(label="MPG", x=unit(-3, "lines"), rot=90)

# Add points
grid.points(x=mtcars$wt, y=mtcars$mpg, pch=16)


# Work from before
pushViewport(plotViewport(c(5, 4, 2, 2)))
grid.rect(gp = gpar())
pushViewport(dataViewport(xData = mtcars$wt, yData = mtcars$mpg))
grid.xaxis()
grid.yaxis()

# Work from before - add names
grid.text("Weight", y = unit(-3, "lines"), name = "xaxis")
grid.text("MPG", x = unit(-3, "lines"), rot = 90, name = "yaxis")
grid.points(x = mtcars$wt, y = mtcars$mpg, pch = 16, name = "datapoints")

# Edit "xaxis"
grid.edit("xaxis", label="Miles/(US) gallon")

# Edit "yaxis"
grid.edit("yaxis", label="Weight (1000 lbs)")

# Edit "datapoints"
grid.edit("datapoints", gp=gpar(col="#C3212766", cex=2))


# A simple plot p
p <- ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) + geom_point()

# Create gtab with ggplotGrob()
gtab <- ggplotGrob(p)

# Print out gtab
gtab

## TableGrob (10 x 9) "layout": 18 grobs
##     z         cells       name                                     grob
## 1   0 ( 1-10, 1- 9) background        rect[plot.background..rect.17231]
## 2   5 ( 5- 5, 3- 3)     spacer                           zeroGrob[NULL]
## 3   7 ( 6- 6, 3- 3)     axis-l    absoluteGrob[GRID.absoluteGrob.17207]
## 4   3 ( 7- 7, 3- 3)     spacer                           zeroGrob[NULL]
## 5   6 ( 5- 5, 4- 4)     axis-t                           zeroGrob[NULL]
## 6   1 ( 6- 6, 4- 4)      panel               gTree[panel-1.gTree.17187]
## 7   9 ( 7- 7, 4- 4)     axis-b    absoluteGrob[GRID.absoluteGrob.17200]
## 8   4 ( 5- 5, 5- 5)     spacer                           zeroGrob[NULL]
## 9   8 ( 6- 6, 5- 5)     axis-r                           zeroGrob[NULL]
## 10  2 ( 7- 7, 5- 5)     spacer                           zeroGrob[NULL]
## 11 10 ( 4- 4, 4- 4)     xlab-t                           zeroGrob[NULL]
## 12 11 ( 8- 8, 4- 4)     xlab-b titleGrob[axis.title.x..titleGrob.17190]
## 13 12 ( 6- 6, 2- 2)     ylab-l titleGrob[axis.title.y..titleGrob.17193]
## 14 13 ( 6- 6, 6- 6)     ylab-r                           zeroGrob[NULL]
## 15 14 ( 6- 6, 8- 8)  guide-box                        gtable[guide-box]
## 16 15 ( 3- 3, 4- 4)   subtitle  zeroGrob[plot.subtitle..zeroGrob.17228]
## 17 16 ( 2- 2, 4- 4)      title     zeroGrob[plot.title..zeroGrob.17227]
## 18 17 ( 9- 9, 4- 4)    caption   zeroGrob[plot.caption..zeroGrob.17229]

# Extract the grobs from gtab: gtab
g <- gtab$grobs

# Draw only the legend
grid.draw(g[[15]])


# Code from before
p <- ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) + geom_point()
gtab <- ggplotGrob(p)
g <- gtab$grobs
grid.draw(g[[15]])

# Show layout of g[[15]]
gtable_show_layout(g[[15]])

# Create text grob
my_text <- textGrob(label = "Motor Trend, 1974", gp = gpar(fontsize = 7, col = "gray25"))

# Use gtable_add_grob to modify original gtab
new_legend <- gtable_add_grob(gtab$grobs[[15]], my_text, 3, 2)

# Update in gtab
gtab$grobs[[15]] <- new_legend

# Draw gtab
grid.draw(gtab)

# Simple plot p
p <- ggplot(mtcars, aes(x = wt, y = mpg, col = factor(cyl))) + geom_point()

# Examine class() and names()
class(p)

## [1] "gg"     "ggplot"

names(p)

## [1] "data"        "layers"      "scales"      "mapping"     "theme"      
## [6] "coordinates" "facet"       "plot_env"    "labels"

# Print the scales sub-list
p$scales$scales

## list()

# Update p
p <- p +
  scale_x_continuous("Length", limits = c(4, 8), expand = c(0, 0)) +
  scale_y_continuous("Width", limits = c(2, 4.5), expand = c(0, 0))

# Print the scales sub-list
p$scales$scales

## [[1]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    4 --    8
## 
## [[2]]
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    2 --  4.5

# Box plot of mtcars: p
p <- ggplot(mtcars, aes(x = factor(cyl), y = wt)) + geom_boxplot()

# Create pbuild
pbuild <- ggplot_build(p)

# a list of 3 elements
names(pbuild)

## [1] "data"   "layout" "plot"

# Print out each element in pbuild
pbuild$data

## [[1]]
##    ymin  lower middle   upper ymax            outliers notchupper
## 1 1.513 1.8850  2.200 2.62250 3.19                       2.551336
## 2 2.620 2.8225  3.215 3.44000 3.46                       3.583761
## 3 3.170 3.5325  3.755 4.01375 4.07 5.250, 5.424, 5.345   3.958219
##   notchlower x PANEL group ymin_final ymax_final  xmin  xmax weight colour
## 1   1.848664 1     1     1      1.513      3.190 0.625 1.375      1 grey20
## 2   2.846239 2     1     2      2.620      3.460 1.625 2.375      1 grey20
## 3   3.551781 3     1     3      3.170      5.424 2.625 3.375      1 grey20
##    fill size alpha shape linetype
## 1 white  0.5    NA    19    solid
## 2 white  0.5    NA    19    solid
## 3 white  0.5    NA    19    solid

pbuild$layout

## <ggproto object: Class Layout>
##     facet: <ggproto object: Class FacetNull, Facet>
##         compute_layout: function
##         draw_back: function
##         draw_front: function
##         draw_labels: function
##         draw_panels: function
##         finish_data: function
##         init_scales: function
##         map: function
##         map_data: function
##         params: list
##         render_back: function
##         render_front: function
##         render_panels: function
##         setup_data: function
##         setup_params: function
##         shrink: TRUE
##         train: function
##         train_positions: function
##         train_scales: function
##         vars: function
##         super:  <ggproto object: Class FacetNull, Facet>
##     finish_data: function
##     get_scales: function
##     map: function
##     map_position: function
##     panel_layout: data.frame
##     panel_ranges: list
##     panel_scales: list
##     render: function
##     render_labels: function
##     reset_scales: function
##     setup: function
##     train_position: function
##     train_ranges: function
##     xlabel: function
##     ylabel: function
##     super:  <ggproto object: Class Layout>

pbuild$plot

# Create gtab from pbuild
gtab <- ggplot_gtable(pbuild)

# Draw gtab
grid.draw(gtab)

# Box plot of mtcars: p
p <- ggplot(mtcars, aes(x = factor(cyl), y = wt)) + geom_boxplot()

# Build pdata
pdata <- ggplot_build(p)$data

# Access the first element of the list, a data frame
class(pdata[[1]])

## [1] "data.frame"

# Isolate this data frame
my_df <- pdata[[1]]

# The x labels
my_df$group <- c("4", "6", "8")

# Print out specific variables
my_df[c(1:6, 11)]

##    ymin  lower middle   upper ymax            outliers group
## 1 1.513 1.8850  2.200 2.62250 3.19                         4
## 2 2.620 2.8225  3.215 3.44000 3.46                         6
## 3 3.170 3.5325  3.755 4.01375 4.07 5.250, 5.424, 5.345     8

# Add a theme (legend at the bottom)
g1 <- ggplot(mtcars, aes(wt, mpg, col = cyl)) +
  geom_point(alpha = 0.5) +
  theme(legend.position="bottom")

# Add a theme (no legend)
g2 <- ggplot(mtcars, aes(disp, fill = cyl)) +
  geom_histogram(position = "identity", alpha = 0.5, binwidth = 20) +
  theme(legend.position="none")

# Load gridExtra
library(gridExtra)

# Call grid.arrange()
grid.arrange(g1, g2, ncol=2)

# Definitions of g1 and g2
g1 <- ggplot(mtcars, aes(wt, mpg, col = cyl)) +
  geom_point() +
  theme(legend.position = "bottom")

g2 <- ggplot(mtcars, aes(disp, fill = cyl)) +
  geom_histogram(binwidth = 20) +
  theme(legend.position = "none")

# Extract the legend from g1
my_legend <- ggplotGrob(g1)$grobs[[15]]  

# Create g1_noleg
g1_noleg <- ggplot(mtcars, aes(wt, mpg, col = cyl)) +
  geom_point() +
  theme(legend.position = "none")

# Calculate the height: legend_height
legend_height <- sum(my_legend$heights)

# Arrange g1_noleg, g2 and my_legend
grid.arrange(g1_noleg, g2, my_legend, 
  layout_matrix=matrix(c(1, 3, 2, 3), ncol=2), 
  heights=unit.c(unit(1, "npc") - legend_height, legend_height)
  )

Continuing with:

test_data <- data.frame(x=rep(0, 60), y=rep(0, 60))
test_data$x <- as.numeric(strsplit("2560 ; 2345 ; 1845 ; 2260 ; 2440 ; 2285 ; 2275 ; 2350 ; 2295 ; 1900 ; 2390 ; 2075 ; 2330 ; 3320 ; 2885 ; 3310 ; 2695 ; 2170 ; 2710 ; 2775 ; 2840 ; 2485 ; 2670 ; 2640 ; 2655 ; 3065 ; 2750 ; 2920 ; 2780 ; 2745 ; 3110 ; 2920 ; 2645 ; 2575 ; 2935 ; 2920 ; 2985 ; 3265 ; 2880 ; 2975 ; 3450 ; 3145 ; 3190 ; 3610 ; 2885 ; 3480 ; 3200 ; 2765 ; 3220 ; 3480 ; 3325 ; 3855 ; 3850 ; 3195 ; 3735 ; 3665 ; 3735 ; 3415 ; 3185 ; 3690", " ; ")[[1]])
test_data$y <- as.numeric(strsplit("97 ; 114 ; 81 ; 91 ; 113 ; 97 ; 97 ; 98 ; 109 ; 73 ; 97 ; 89 ; 109 ; 305 ; 153 ; 302 ; 133 ; 97 ; 125 ; 146 ; 107 ; 109 ; 121 ; 151 ; 133 ; 181 ; 141 ; 132 ; 133 ; 122 ; 181 ; 146 ; 151 ; 116 ; 135 ; 122 ; 141 ; 163 ; 151 ; 153 ; 202 ; 180 ; 182 ; 232 ; 143 ; 180 ; 180 ; 151 ; 189 ; 180 ; 231 ; 305 ; 302 ; 151 ; 202 ; 182 ; 181 ; 143 ; 146 ; 146", " ; ")[[1]])


# Call bagplot() on test_data
bagplot(test_data)

# Call compute.bagplot on test_data: bag
bag <- compute.bagplot(test_data)


# Highlight components
points(bag$hull.loop, col = "green", pch = 16)
points(bag$hull.bag, col = "orange", pch = 16)
points(bag$pxy.outlier, col="purple", pch=16)

# Create data frames from matrices
hull.loop <- data.frame(x = bag$hull.loop[,1], y = bag$hull.loop[,2])
hull.bag <- data.frame(x = bag$hull.bag[,1], y = bag$hull.bag[,2])
pxy.outlier <- data.frame(x = bag$pxy.outlier[, 1], y=bag$pxy.outlier[, 2])

# Finish the ggplot command
ggplot(test_data, aes(x=x, y=y)) +
  geom_polygon(data = hull.loop, fill = "green") +
  geom_polygon(data = hull.bag, fill = "orange") +
  geom_point(data=pxy.outlier, col="purple", pch=16, cex=1.5)

# ggproto for StatLoop (hull.loop)
StatLoop <- ggproto("StatLoop", Stat,
                    required_aes = c("x", "y"),
                    compute_group = function(data, scales) {
                      bag <- compute.bagplot(x = data$x, y = data$y)
                      data.frame(x = bag$hull.loop[,1], y = bag$hull.loop[,2])
                    })

# ggproto for StatBag (hull.bag)
StatBag <- ggproto("StatBag", Stat,
                   required_aes = c("x", "y"),
                   compute_group = function(data, scales) {
                     bag <- compute.bagplot(x = data$x, y = data$y)
                     data.frame(x = bag$hull.bag[, 1], y = bag$hull.bag[, 2])
                   })

# ggproto for StatOut (pxy.outlier)
StatOut <- ggproto("StatOut", Stat,
                   required_aes = c("x", "y"),
                   compute_group = function(data, scales) {
                     bag <- compute.bagplot(x = data$x, y = data$y)
                     data.frame(x = bag$pxy.outlier[, 1], y = bag$pxy.outlier[, 2])
                   })


# Combine ggproto objects in layers to build stat_bag()
stat_bag <- function(mapping = NULL, data = NULL, geom = "polygon",
                     position = "identity", na.rm = FALSE, show.legend = NA,
                     inherit.aes = TRUE, loop = FALSE, ...) {
  list(
    # StatLoop layer
    layer(
      stat = StatLoop, data = data, mapping = mapping, geom = geom, 
      position = position, show.legend = show.legend, inherit.aes = inherit.aes,
      params = list(na.rm = na.rm, alpha=0.35, col=NA, ...)
    ),
    # StatBag layer
    layer(
      stat = StatBag, data = data, mapping = mapping, geom = geom, 
      position = position, show.legend = show.legend, inherit.aes = inherit.aes,
      params = list(na.rm = na.rm, alpha=0.35, col=NA, ...)
    ),
    # StatOut layer
    layer(
      stat = StatOut, data = data, mapping = mapping, geom = "point", 
      position = position, show.legend = show.legend, inherit.aes = inherit.aes,
      params = list(na.rm = na.rm, alpha = 0.7, col = NA, shape = 21, ...)
    )
  )
}


# Previous method
ggplot(test_data, aes(x = x,  y = y)) +
  geom_polygon(data = hull.loop, fill = "green") +
  geom_polygon(data = hull.bag, fill = "orange") +
  geom_point(data = pxy.outlier, col = "purple", pch = 16, cex = 1.5)

# stat_bag
ggplot(test_data, aes(x = x,  y = y)) +
  stat_bag(fill = "black")

## Warning: Removed 4 rows containing missing values (geom_point).

# stat_bag on test_data2
# Would require brining over test_data2 (point is that it is now transferrable)
# ggplot(test_data2, aes(x = x,  y = y, fill=treatment)) +
#  stat_bag()


# Data set NYNEWYOR.txt downloaded from US Cities/New York/NYC available at:
# http://academic.udayton.edu/kissock/http/Weather/

# Import weather data
weather <- read.fwf("NYNEWYOR.txt",
                    header = FALSE,
                    col.names = c("month", "day", "year", "temp"),
                    widths = c(14, 14, 13, 4)
                    )

# Check structure of weather
str(weather)

## 'data.frame':    8009 obs. of  4 variables:
##  $ month: num  1 1 1 1 1 1 1 1 1 1 ...
##  $ day  : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ year : num  1995 1995 1995 1995 1995 ...
##  $ temp : num  44 41 28 31 21 27 42 35 34 29 ...

# Create past with two filter() calls
past <- weather %>%
  filter(month != 2 | day != 29) %>%
  filter(year != max(weather$year))
  
# Check structure of past
str(past)

## 'data.frame':    7665 obs. of  4 variables:
##  $ month: num  1 1 1 1 1 1 1 1 1 1 ...
##  $ day  : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ year : num  1995 1995 1995 1995 1995 ...
##  $ temp : num  44 41 28 31 21 27 42 35 34 29 ...

# Create new version of past
past_summ <- past %>%
  group_by(year) %>%
  mutate(yearday=1:length(day)) %>%
  ungroup() %>%
  filter(temp != -99) %>%
  group_by(yearday) %>%
  mutate(max = max(temp),
         min = min(temp),
         avg = mean(temp),
         CI_lower = Hmisc::smean.cl.normal(temp)[2],
         CI_upper = Hmisc::smean.cl.normal(temp)[3]
         ) %>%
  ungroup()

# Structure of past_summ
str(past_summ)

## Classes 'tbl_df', 'tbl' and 'data.frame':    7645 obs. of  10 variables:
##  $ month   : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ day     : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ year    : num  1995 1995 1995 1995 1995 ...
##  $ temp    : num  44 41 28 31 21 27 42 35 34 29 ...
##  $ yearday : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ max     : num  51 48 57 55 56 62 52 57 54 47 ...
##  $ min     : num  17 15 16 15 21 14 14 12 21 8.5 ...
##  $ avg     : num  35.6 35.4 34.9 35.1 35.9 ...
##  $ CI_lower: num  31 31.6 29.7 29.9 31.9 ...
##  $ CI_upper: num  40.1 39.2 40 40.4 39.9 ...

# Adapt historical plot
ggplot(past_summ, aes(x = yearday, y = temp)) +
  geom_point(col = "#EED8AE", alpha = 0.3, shape=16) +
  geom_linerange(aes(ymin = CI_lower, ymax = CI_upper), col = "#8B7E66")

# Create present
present <- weather %>%
  filter(!(month == 2 & day == 29)) %>%
  filter(year == max(year)) %>%
  group_by(year) %>%
  mutate(yearday = 1:length(day)) %>%
  ungroup() %>%
  filter(temp != -99)

# Add geom_line to ggplot command
ggplot(past_summ, aes(x = yearday, y = temp)) + 
  geom_point(col = "#EED8AE", alpha = 0.3, shape = 16) +
  geom_linerange(aes(ymin = CI_lower, ymax = CI_upper), col = "#8B7E66") +
  geom_line(data=present, aes(x = yearday, y=temp))

# Create past_highs
past_highs <- past_summ %>%
  group_by(yearday) %>%
  summarise(past_high = max(temp))

# Create record_high
record_high <- present %>%
  left_join(past_highs, by="yearday") %>%
  filter(temp > past_high)

# Add record_high information to plot
ggplot(past_summ, aes(x = yearday, y = temp)) + 
  geom_point(col = "#EED8AE", alpha = 0.3, shape = 16) +
  geom_linerange(aes(ymin = CI_lower, ymax = CI_upper), col = "#8B7E66") +
  geom_line(data = present) + 
  geom_point(data=record_high, col = "#CD2626")

# Create past_extremes
past_extremes <- past_summ %>%
  group_by(yearday) %>%
  summarise(past_low = min(temp),
            past_high = max(temp)
            )

# Create record_high_low
record_high_low <- present %>%
  left_join(past_extremes, by="yearday") %>%
  mutate(record = ifelse(temp < past_low, 
                         "#0000CD",
                         ifelse(temp > past_high, 
                                "#CD2626", 
                                "#00000000")
                                )
                                )

# Structure of record_high_low
str(record_high_low)

## Classes 'tbl_df', 'tbl' and 'data.frame':    338 obs. of  8 variables:
##  $ month    : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ day      : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ year     : num  2016 2016 2016 2016 2016 ...
##  $ temp     : num  41 37 40 33 19 32 39 40 43 50 ...
##  $ yearday  : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ past_low : num  17 15 16 15 21 14 14 12 21 8.5 ...
##  $ past_high: num  51 48 57 55 56 62 52 57 54 47 ...
##  $ record   : chr  "#00000000" "#00000000" "#00000000" "#00000000" ...

# Add point layer of record_high_low
ggplot(past_summ, aes(x = yearday, y = temp)) + 
  geom_point(col = "#EED8AE", alpha = 0.3, shape = 16) +
  geom_linerange(aes(ymin = CI_lower, ymax = CI_upper), col = "#8B7E66") +
  geom_line(data = present) +
  geom_point(data=record_high_low, aes(col=record)) + 
  scale_color_identity()



# Finish the function draw_pop_legend
draw_pop_legend <- function(x = 0.6, y = 0.2, width = 0.2, height = 0.2, fontsize = 10) {
  
  # Finish viewport() function
  pushViewport(viewport(x = x, y = y, width = width, height = height, just = "center"))

  legend_labels <- c("Past record high",
                     "95% CI range",
                     "Current year",
                     "Past years",
                     "Past record low")

  legend_position <- c(0.9, 0.7, 0.5, 0.2, 0.1)
  
  # Finish grid.text() function
  grid.text(legend_labels, x = 0.12, y = legend_position, 
            just = "left", gp = gpar(fontsize = fontsize, col = "grey20"))
  
  # Position dots, rectangle and line
  point_position_y <- c(0.1, 0.2, 0.9)
  point_position_x <- rep(0.06, length(point_position_y))
  grid.points(x = point_position_x, y = point_position_y, pch = 16,
              gp = gpar(col = c("#0000CD", "#EED8AE", "#CD2626")))
  grid.rect(x = 0.06, y = 0.5, width = 0.06, height = 0.4,
            gp = gpar(col = NA, fill = "#8B7E66"))
  grid.lines(x = c(0.03, 0.09), y = c(0.5, 0.5),
             gp = gpar(col = "black", lwd = 3))
  
  # Add popViewport() for bookkeeping
  popViewport()
  
}

# Call draw_pop_legend()
draw_pop_legend()

# Finish the clean_weather function
clean_weather <- function(file) {
  weather <- read.fwf(file,
                      header=FALSE,
                      col.names = c("month", "day", "year", "temp"),
                      widths = c(14, 14, 13, 4))
  weather %>%
    filter(month != 2 | day != 29) %>%
    group_by(year) %>%
    mutate(yearday = 1:length(day)) %>%
    ungroup() %>%
    filter(temp != -99)
}

# Import NYNEWYOR.txt: my_data
my_data <- clean_weather(file="NYNEWYOR.txt")


# Create the stats object
StatHistorical <- ggproto("StatHistorical", Stat,
                    compute_group = function(data, scales, params) {
                      data <- data %>%
                        filter(year != max(year)) %>%
                        group_by(x) %>%
                        mutate(ymin = Hmisc::smean.cl.normal(y)[3],
                               ymax = Hmisc::smean.cl.normal(y)[2]) %>%
                        ungroup()
                    },
                    required_aes = c("x", "y", "year"))

# Create the layer
stat_historical <- function(mapping = NULL, data = NULL, geom = "point",
                            position = "identity", na.rm = FALSE, show.legend = NA, 
                            inherit.aes = TRUE, ...) {
  list(
    layer(
      stat = "identity", data = data, mapping = mapping, geom = geom,
      position = position, show.legend = show.legend, inherit.aes = inherit.aes,
      params = list(na.rm = na.rm, col = "#EED8AE", alpha = 0.3, shape = 16, ...)
    ),
    layer(
      stat = StatHistorical, data = data, mapping = mapping, geom = "linerange",
      position = position, show.legend = show.legend, inherit.aes = inherit.aes,
      params = list(na.rm = na.rm, col = "#8B7E66", ...)
    )
  )
}

# Build the plot
my_data <- clean_weather("NYNEWYOR.txt")
ggplot(my_data, aes(x = yearday, y = temp, year = year)) +
  stat_historical()

# Create the stats object
StatPresent <- ggproto("StatPresent", Stat,
                       compute_group = function(data, scales, params) {
                         data <- filter(data, year == max(year))
                       },
                       required_aes = c("x", "y", "year")
                       )

# Create the layer
stat_present <- function(mapping = NULL, data = NULL, geom = "line",
                       position = "identity", na.rm = FALSE, show.legend = NA, 
                       inherit.aes = TRUE, ...) {
  layer(
    stat = StatPresent, data = data, mapping = mapping, geom = geom,
    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
    params = list(na.rm = na.rm, ...)
  )
}

# Build the plot
my_data <- clean_weather("NYNEWYOR.txt")
ggplot(my_data, aes(x = yearday, y = temp, year = year)) +
  stat_historical() + 
  stat_present()

# Create the stats object
StatExtremes <- ggproto("StatExtremes", Stat,
                        compute_group = function(data, scales, params) {
                          
                          present <- data %>%
                            filter(year == max(year)) 
                          
                          past <- data %>%
                            filter(year != max(year)) 
                          
                          past_extremes <- past %>%
                            group_by(x) %>%
                            summarise(past_low = min(y),
                                      past_high = max(y))
                          
                          # transform data to contain extremes
                          data <- present %>%
                            left_join(past_extremes, by="x") %>%
                            mutate(record = ifelse(y < past_low, 
                                                   "#0000CD", 
                                                   ifelse(y > past_high, 
                                                          "#CD2626", 
                                                          "#00000000"
                                                          )
                                                          )
                                                          )
                        },
                        required_aes = c("x", "y", "year"))

# Create the layer
stat_extremes <- function(mapping = NULL, data = NULL, geom = "point",
                          position = "identity", na.rm = FALSE, show.legend = NA, 
                          inherit.aes = TRUE, ...) {
  layer(
    stat = StatExtremes, data = data, mapping = mapping, geom = geom,
    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
    params = list(na.rm = na.rm, ...)
  )
}

# Build the plot
my_data <- clean_weather("NYNEWYOR.txt")
ggplot(my_data, aes(x = yearday, y = temp, year = year)) +
  stat_historical() +
  stat_present() +
  stat_extremes(aes(col=..record..)) + 
  scale_color_identity()

# Data sets FRPARIS.txt, ILREYKJV.txt, UKLONDON.txt downloaded from International Cities at:
# http://academic.udayton.edu/kissock/http/Weather/citylistWorld.htm

# File paths of all datasets
my_files <- c("NYNEWYOR.txt","FRPARIS.txt", "ILREYKJV.txt", "UKLONDON.txt")

# Build my_data with a for loop
my_data <- NULL
for (file in my_files) {
  temp <- clean_weather(file)
  temp$id <- sub(".txt", "", file)
  my_data <- rbind(my_data, temp)
}

# Build the final plot, from scratch!
ggplot(data=my_data, aes(x=yearday, y=temp, year=year)) + 
  stat_historical() +
  stat_present() +
  stat_extremes(aes(col=..record..)) +
  scale_color_identity() +
  facet_wrap(~id, ncol=2)

Data Visualization (ggvis)

The ggvis is based on a “grammar of graphics” and is closely linked to ggplot2 (both designed by Wickham). The objective for ggvis is to combine the analytic power of R with the visual power of Javascript.

Broadly, the “grammar of graphics” includes several layers such as Data, Coordinate System, Marks, Properties, and the like. This is similar to ggplot2 though with a modified syntax that is more in line with dplyr chaining:

myData %>% ggvis(~myX, ~myY, fill = ~myFill, …) %>% layer_myMarkChoice()

Note that the := operator is the static assignment operator, ensuring that a call to “red” means the color “red” and not merely a character vector coerced to the required length with every entry being “red”. The ~ symbolizes that this is a variable in my dataset. So ~red would mean the variable red in the dataset undergoing plotting.

Some basic example code includes:

library(ggvis)

## Warning: package 'ggvis' was built under R version 3.2.5

## 
## Attaching package: 'ggvis'

## The following object is masked from 'package:ggplot2':
## 
##     resolution

data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# Change the code below to make a graph with red points
mtcars %>% ggvis(~wt, ~mpg, fill := "red") %>% layer_points()

• Renderer: SVG | Canvas
• Download

# Change the code below draw smooths instead of points
mtcars %>% ggvis(~wt, ~mpg) %>% layer_smooths()

• Renderer: SVG | Canvas
• Download

# Change the code below to make a graph containing both points and a smoothed summary line
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>% layer_smooths()

• Renderer: SVG | Canvas
• Download

data(pressure)
str(pressure)

## 'data.frame':    19 obs. of  2 variables:
##  $ temperature: num  0 20 40 60 80 100 120 140 160 180 ...
##  $ pressure   : num  0.0002 0.0012 0.006 0.03 0.09 0.27 0.75 1.85 4.2 8.8 ...

# Adapt the code: show bars instead of points
pressure %>% ggvis(~temperature, ~pressure) %>% layer_bars()

• Renderer: SVG | Canvas
• Download

# Adapt the codee: show lines instead of points
pressure %>% ggvis(~temperature, ~pressure) %>% layer_lines()

• Renderer: SVG | Canvas
• Download

# Extend the code: map the fill property to the temperature variable
pressure %>% ggvis(~temperature, ~pressure, fill=~temperature) %>% layer_points()

• Renderer: SVG | Canvas
• Download

# Extend the code: map the size property to the pressure variable
pressure %>% ggvis(~temperature, ~pressure, size=~pressure) %>% layer_points()

• Renderer: SVG | Canvas
• Download

There are three main new operators in ggvis (relative to ggplot):

• %>% (piping from magrittr)
  
• ~ (for defining variables as being from the dataset)
  
• := (for setting of properties)
  • Can think of having a data space (e.g., species or size) and a visual space (e.g., species is filled by color or size is represented by shape)
    
  • The straight-up equal sign (=) and the tilde tell ggvis that the variables exists in the data space
    
  • Using the setting operator (:=) you can make an assignment to something that is not in the data space; for example fill := “red” or size := 100 (purely a visual space assignment)

The line is a special type of mark (second most common after points) - stroke, strokeWidth, strokeOpacity, strokeDash, fill, fillOpacity

Other forms include:

• Paths, sort of like lines, but connecting points in their same order as the dataset
  
• Ribbons, which have bounding
  
• Smooths, which have loess (or lm or etc.) added to the data
  
• Model predictions, which add a general model prediction

Example code includes:

data(faithful)
str(faithful)

## 'data.frame':    272 obs. of  2 variables:
##  $ eruptions: num  3.6 1.8 3.33 2.28 4.53 ...
##  $ waiting  : num  79 54 74 62 85 55 88 85 51 85 ...

faithful %>% ggvis(~waiting, ~eruptions) %>% layer_points()

• Renderer: SVG | Canvas
• Download

faithful %>% 
    ggvis(~waiting, ~eruptions, size = ~eruptions) %>% 
    layer_points(opacity := 0.5, fill := "blue", stroke := "black")

• Renderer: SVG | Canvas
• Download

faithful %>% 
    ggvis(~waiting, ~eruptions, fillOpacity = ~eruptions) %>% 
    layer_points(size := 100, fill := "red", stroke := "red", shape := "cross")

• Renderer: SVG | Canvas
• Download

data(pressure)
str(pressure)

## 'data.frame':    19 obs. of  2 variables:
##  $ temperature: num  0 20 40 60 80 100 120 140 160 180 ...
##  $ pressure   : num  0.0002 0.0012 0.006 0.03 0.09 0.27 0.75 1.85 4.2 8.8 ...

# Modify this graph to map the size property to the pressure variable
pressure %>% ggvis(~temperature, ~pressure, size = ~pressure) %>% layer_points()

• Renderer: SVG | Canvas
• Download

# Modify this graph by setting the size property
pressure %>% ggvis(~temperature, ~pressure, size := 100) %>% layer_points()

• Renderer: SVG | Canvas
• Download

# Fix this code to set the fill property to red
pressure %>% ggvis(~temperature, ~pressure, fill := "red") %>% layer_points()

• Renderer: SVG | Canvas
• Download

pressure %>% 
    ggvis(~temperature, ~pressure) %>% 
    layer_lines(stroke := "red", strokeWidth := 2, strokeDash := 6)

• Renderer: SVG | Canvas
• Download

# texas %>% ggvis(~long, ~lat) %>% layer_paths(fill := "darkorange")


data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

mtcars %>% compute_smooth(mpg ~ wt)

##       pred_    resp_
## 1  1.513000 32.08897
## 2  1.562506 31.68786
## 3  1.612013 31.28163
## 4  1.661519 30.87037
## 5  1.711025 30.45419
## 6  1.760532 30.03318
## 7  1.810038 29.60745
## 8  1.859544 29.17711
## 9  1.909051 28.74224
## 10 1.958557 28.30017
## 11 2.008063 27.83462
## 12 2.057570 27.34766
## 13 2.107076 26.84498
## 14 2.156582 26.33229
## 15 2.206089 25.81529
## 16 2.255595 25.29968
## 17 2.305101 24.79115
## 18 2.354608 24.29542
## 19 2.404114 23.81818
## 20 2.453620 23.36514
## 21 2.503127 22.95525
## 22 2.552633 22.61385
## 23 2.602139 22.32759
## 24 2.651646 22.08176
## 25 2.701152 21.86167
## 26 2.750658 21.65260
## 27 2.800165 21.43987
## 28 2.849671 21.20875
## 29 2.899177 20.95334
## 30 2.948684 20.71584
## 31 2.998190 20.49571
## 32 3.047696 20.28293
## 33 3.097203 20.06753
## 34 3.146709 19.83950
## 35 3.196215 19.58885
## 36 3.245722 19.29716
## 37 3.295228 18.94441
## 38 3.344734 18.56700
## 39 3.394241 18.20570
## 40 3.443747 17.90090
## 41 3.493253 17.62060
## 42 3.542759 17.34002
## 43 3.592266 17.07908
## 44 3.641772 16.81759
## 45 3.691278 16.55757
## 46 3.740785 16.30833
## 47 3.790291 16.07916
## 48 3.839797 15.87937
## 49 3.889304 15.70181
## 50 3.938810 15.52594
## 51 3.988316 15.35173
## 52 4.037823 15.17933
## 53 4.087329 15.00894
## 54 4.136835 14.84072
## 55 4.186342 14.67484
## 56 4.235848 14.51148
## 57 4.285354 14.35082
## 58 4.334861 14.19302
## 59 4.384367 14.03826
## 60 4.433873 13.88672
## 61 4.483380 13.73856
## 62 4.532886 13.59396
## 63 4.582392 13.45310
## 64 4.631899 13.31614
## 65 4.681405 13.18326
## 66 4.730911 13.05464
## 67 4.780418 12.93045
## 68 4.829924 12.81086
## 69 4.879430 12.69604
## 70 4.928937 12.58617
## 71 4.978443 12.48143
## 72 5.027949 12.38198
## 73 5.077456 12.28799
## 74 5.126962 12.19966
## 75 5.176468 12.11713
## 76 5.225975 12.04060
## 77 5.275481 11.97023
## 78 5.324987 11.90620
## 79 5.374494 11.84868
## 80 5.424000 11.79784

# Extend with ggvis() and layer_lines()
mtcars %>% compute_smooth(mpg ~ wt) %>% ggvis(~pred_, ~resp_) %>% layer_lines()

• Renderer: SVG | Canvas
• Download

# Extend with layer_points() and layer_smooths()
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>% layer_smooths()

• Renderer: SVG | Canvas
• Download

Behind the scenes, ggvis uses several compute functions to help with visualizations:

• Using a computation such as a loess (smooth) means that you then map the marks to the smooth data and not the original data
  
• The ggvis library only has five key marks - points, paths, ribbons, rects, and text
  
• These are extended by a library of compute functions - smooths, model_predictions, bars, histograms, densities, etc.
  
• Typically, the compute functions are called automatically by a layer called by the user

The ggvis library is especially well designed to interact with the dplyr library (Hadley Wickham):

• For example, layer_lines() is just arrange() %>% layer_paths(), since the arrange puts it in the proper x-axis order
  
• The dplyr::group_by() offers the opportunities to treat different subsets of the data differently

Example code includes:

data(faithful)
str(faithful)

## 'data.frame':    272 obs. of  2 variables:
##  $ eruptions: num  3.6 1.8 3.33 2.28 4.53 ...
##  $ waiting  : num  79 54 74 62 85 55 88 85 51 85 ...

faithful %>% ggvis(~waiting) %>% layer_histograms(width = 5)

• Renderer: SVG | Canvas
• Download

# Finish the command
faithful %>%
  compute_bin(~waiting, width = 5) %>%
  ggvis(x = ~xmin_, x2 = ~xmax_, y = 0, y2 = ~count_) %>%
  layer_rects()

• Renderer: SVG | Canvas
• Download

# Build the density plot
faithful %>% ggvis(~waiting, fill := "green") %>% layer_densities()

• Renderer: SVG | Canvas
• Download

data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

mtcars %>% 
  ggvis(x = ~factor(cyl)) %>% 
  layer_bars()

• Renderer: SVG | Canvas
• Download

# Instruction 1
mtcars %>% 
    group_by(cyl) %>% 
    ggvis(~mpg, ~wt, stroke = ~factor(cyl)) %>% 
    layer_smooths()

## Warning in rbind_all(out[[1]]): Unequal factor levels: coercing to
## character

• Renderer: SVG | Canvas
• Download

# Instruction 2
mtcars %>% 
    group_by(cyl) %>% 
    ggvis(~mpg, fill = ~factor(cyl)) %>% 
    layer_densities()

## Warning in rbind_all(out[[1]]): Unequal factor levels: coercing to
## character

• Renderer: SVG | Canvas
• Download

mtcars %>% 
    group_by(cyl, am) %>% 
    ggvis(~mpg, fill = ~interaction(cyl, am)) %>% 
    layer_densities()

## Warning in rbind_all(out[[1]]): Unequal factor levels: coercing to
## character

• Renderer: SVG | Canvas
• Download

Can add interactivity to plots in ggvis:

• For example, a slider to change a key parameter such as “span”
  
• Can actually add any portion of ggvis to an interactive user control
  
• While the interactive ggvis is active, R will remain busy waiting to re-plot on new user inputs
  • Need to hit escape and/or the red stop sign to return control to R
    
• Based on the Shiny framework
  • Can send to other R users
    
  • Can use Shiny server

Multi-layered ggvis plots:

• With ggvis, this is as simple as adding the second layer after the first layer
  
• Keep adding layers with the piping operator

Example code includes:

data(faithful)
str(faithful)

## 'data.frame':    272 obs. of  2 variables:
##  $ eruptions: num  3.6 1.8 3.33 2.28 4.53 ...
##  $ waiting  : num  79 54 74 62 85 55 88 85 51 85 ...

data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# Adapt the code: set fill with a select box
faithful %>% 
  ggvis(~waiting, ~eruptions, fillOpacity := 0.5, 
        shape := input_select(label = "Choose shape:", 
                              choices = c("circle", "square", "cross", 
                                          "diamond", "triangle-up", "triangle-down"
                                          )
                              ), 
        fill := input_select(label = "Choose color:", 
                             choices = c("black", "red", "blue", "green")
                             )
        ) %>% 
  layer_points()

## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

• Renderer: SVG | Canvas
• Download

# Add radio buttons to control the fill of the plot
mtcars %>% 
  ggvis(~mpg, ~wt,
        fill := input_radiobuttons(label = "Choose color:", 
                                   choices = c("black", "red", "blue", "green")
                                   )
        ) %>% 
  layer_points()

## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

• Renderer: SVG | Canvas
• Download

mtcars %>% 
  ggvis(~mpg, ~wt, 
        fill := input_text(label = "Choose color:", value = "black")) %>% 
  layer_points()

## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

• Renderer: SVG | Canvas
• Download

# Map the fill property to a select box that returns variable names
mtcars %>% 
  ggvis(~mpg, ~wt, fill = input_select(label = "Choose fill variable:", 
                                       choices = names(mtcars), map=as.name
                                       )
        ) %>% 
  layer_points()

## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

• Renderer: SVG | Canvas
• Download

# Map the bindwidth to a numeric field ("Choose a binwidth:")
mtcars %>% 
  ggvis(~mpg) %>% 
  layer_histograms(width = input_numeric(label = "Choose a binwidth:", value = 1))

## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

• Renderer: SVG | Canvas
• Download

# Map the binwidth to a slider bar ("Choose a binwidth:") with the correct specifications
mtcars %>% 
  ggvis(~mpg) %>% 
  layer_histograms(width = input_slider(label = "Choose a binwidth:", 1, 20))

## Warning: Can't output dynamic/interactive ggvis plots in a knitr document.
## Generating a static (non-dynamic, non-interactive) version of the plot.

• Renderer: SVG | Canvas
• Download

# Add a layer of points to the graph below.
pressure %>% 
  ggvis(~temperature, ~pressure, stroke := "skyblue") %>% 
  layer_lines() %>%
  layer_points()

• Renderer: SVG | Canvas
• Download

# Copy and adapt so that only the lines layer uses a skyblue stroke.
pressure %>% 
  ggvis(~temperature, ~pressure) %>% 
  layer_lines(stroke := "skyblue") %>%
  layer_points()

• Renderer: SVG | Canvas
• Download

# Rewrite the code below so that only the points layer uses the shape property.
pressure %>% 
  ggvis(~temperature, ~pressure) %>% 
  layer_lines(stroke := "skyblue") %>% 
  layer_points(shape := "triangle-up")

• Renderer: SVG | Canvas
• Download

# Refactor the code for the graph below to make it as concise as possible
pressure %>% 
  ggvis(~temperature, ~pressure, stroke := "skyblue", strokeOpacity := 0.5, strokeWidth := 5) %>% 
  layer_lines() %>% 
  layer_points(fill = ~temperature, 
              shape := "triangle-up", 
              size := 300)

• Renderer: SVG | Canvas
• Download

# Add more layers to the line plot
pressure %>%
  ggvis(~temperature, ~pressure) %>%
  layer_lines(opacity := 0.5) %>%
  layer_points() %>%
  layer_model_predictions(model = "lm", stroke := "navy") %>%
  layer_smooths(stroke := "skyblue")

## Guessing formula = pressure ~ temperature

• Renderer: SVG | Canvas
• Download

The add_axis() function can be used to change the titles and axis labels:

• Can also change the ticks and the location (e.g., top/bottom) of where the axes appear on the plot
  
• add_axis(“x”, title = “axis title”, values = c(1, 2, 3), subdivide = 5, orient = “top”)
  
• The first argument specifies which axis to customize
  • title - the title of the axis you specified in the first argument
    
  • values - determine where labelled tick marks will appear on each axis
    
  • subdivide - insert unlabelled tick marks between the labelled tick marks on an axis
    
  • orient - control where the axis appears. For the x axis, you can use “top” or “bottom”, for the y axis, you can use “left” or “right”

The add_legends() function can help with cleaning up legends (make them look tidier). This is similar to the arguments passed to the “adding an axis” above.

Can also customize the scales (relationships between data spaces and visual spaces) for the data:

• Mappings go between the data space (e.g., species) and the visual space (e.g., fill color)
  
• The default mapping is handled in ggvis by a function called a scale
  
• The scale_nominal() command can change the mapping
  • scale_nominal(“fill”, range = c(“yellow”, “orange”, “red”))

Example code includes:

data(faithful)
str(faithful)

## 'data.frame':    272 obs. of  2 variables:
##  $ eruptions: num  3.6 1.8 3.33 2.28 4.53 ...
##  $ waiting  : num  79 54 74 62 85 55 88 85 51 85 ...

# Defaulted axis
faithful %>% 
  ggvis(~waiting, ~eruptions) %>% 
  layer_points()

• Renderer: SVG | Canvas
• Download

# Customized axis
faithful %>% 
  ggvis(~waiting, ~eruptions) %>% 
  layer_points() %>%
  add_axis("x", title="Time since previous eruption (m)", 
           values=c(50, 60, 70, 80, 90), subdivide=9, orient="top"
           ) %>%
  add_axis("y", title="Duration of eruption (m)", values=c(2, 3, 4, 5), 
           subdivide=9, orient="right"
           )

• Renderer: SVG | Canvas
• Download

data(pressure)
str(pressure)

## 'data.frame':    19 obs. of  2 variables:
##  $ temperature: num  0 20 40 60 80 100 120 140 160 180 ...
##  $ pressure   : num  0.0002 0.0012 0.006 0.03 0.09 0.27 0.75 1.85 4.2 8.8 ...

# Add a legend
faithful %>% 
  ggvis(~waiting, ~eruptions, opacity := 0.6, 
        fill = ~factor(round(eruptions))) %>% 
  layer_points() %>%
  add_legend("fill", title="~ duration (m)", orient="left")

• Renderer: SVG | Canvas
• Download

# Original code with jumbled legends
faithful %>% 
  ggvis(~waiting, ~eruptions, opacity := 0.6, 
        fill = ~factor(round(eruptions)), shape = ~factor(round(eruptions)), 
        size = ~round(eruptions))  %>%
  layer_points()

• Renderer: SVG | Canvas
• Download

# Fix the legend
faithful %>% 
  ggvis(~waiting, ~eruptions, opacity := 0.6, 
        fill = ~factor(round(eruptions)), shape = ~factor(round(eruptions)), 
        size = ~round(eruptions))  %>%
  layer_points() %>%
  add_legend(c("fill", "shape", "size"), title="~ duration (m)")

• Renderer: SVG | Canvas
• Download

data(mtcars)
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

# Add a scale_numeric()
mtcars %>% 
  ggvis(~wt, ~mpg, fill = ~disp, stroke = ~disp, strokeWidth := 2) %>%
  layer_points() %>%
  scale_numeric("fill", range = c("red", "yellow")) %>%
  scale_numeric("stroke", range = c("darkred", "orange"))

• Renderer: SVG | Canvas
• Download

# Add a scale_numeric()
mtcars %>% ggvis(~wt, ~mpg, fill = ~hp) %>%
  layer_points() %>%
  scale_numeric("fill", range=c("green", "beige"))

• Renderer: SVG | Canvas
• Download

# Add a scale_nominal()
mtcars %>% ggvis(~wt, ~mpg, fill = ~factor(cyl)) %>%
  layer_points() %>%
  scale_nominal("fill", range=c("purple", "blue", "green"))

• Renderer: SVG | Canvas
• Download

# Original plot becomes too transparent
mtcars %>% ggvis(x = ~wt, y = ~mpg, fill = ~factor(cyl), opacity = ~hp) %>%
  layer_points()

• Renderer: SVG | Canvas
• Download

# Range to prevent overly transparent data points
mtcars %>% ggvis(x = ~wt, y = ~mpg, fill = ~factor(cyl), opacity = ~hp) %>%
  layer_points() %>%
  scale_numeric("opacity", range=c(0.2, 1))

• Renderer: SVG | Canvas
• Download

mtcars %>% ggvis(~wt, ~mpg, fill = ~disp) %>%
  layer_points() %>%
  scale_numeric("y", domain = c(0, NA)) %>%  # NA means top-of-data-range
  scale_numeric("x", domain = c(0, 6))

• Renderer: SVG | Canvas
• Download

mtcars$color <- c('red' , 'teal' , '#cccccc' , 'tan' , 'red' , 'teal' , '#cccccc' , 'tan' , 
                  'red' , 'teal' , '#cccccc' , 'tan' , 'red' , 'teal' , '#cccccc' , 'tan' , 
                  'red' , 'teal' , '#cccccc' , 'tan' , 'red' , 'teal' , '#cccccc' , 'tan' , 
                  'red' , 'teal' , '#cccccc' , 'tan' , 'red' , 'teal' , '#cccccc' , 'tan'
                  )

# Using fill by mapping the "color" variable to the ggvis scales
mtcars %>% 
  ggvis(x = ~wt, y = ~mpg, fill = ~color) %>% 
  layer_points()

• Renderer: SVG | Canvas
• Download

# Using fill based directly on the values in the "color" variable
mtcars %>% 
  ggvis(x = ~wt, y = ~mpg, fill := ~color) %>% 
  layer_points()

• Renderer: SVG | Canvas
• Download

Geospatial data (ggplot2, ggmap, etc.)

Chapter 1 - Basic Mapping (ggplot2, ggmap)

Spatial data is data associated with locations (geo-spatial if it lies on the surface of the earth):

• Lat/Lon is a coordinate reference system (CRS); always need to convert addressed to a CRS
  
• Often, directions are dropped; positive means north or east, and negative means south or west
  • Further, sometimes the listing is long, lat reflecting that longitude is more like the “x-axis” of the earth
    
• Point data is where a single lat/lon defines the point coordinate
  
• The ggmap() package adds maps from map services as a layer that can be shown in a ggplot (or other plot)

There are options to control what is returned by get_map (defualt is terrain map from google):

• These are controlled with maptype= and source= options inside get_map()
  
• The gg_map() can further be controlled using base_layer=
  
• Frequently, there would be a base_layer=ggplot() to make the aes() and data frame from ggplot a perpetual part of the base
  
• There is also a qmplot, similar to how qplot is a shortcut (with many fewer options . . . ) for ggplot

Common types of spatial data:

• Point data - each point has associated data (common, like previous examples)
  
• Line data - described by multiple points connected by straight lines (connection of points might describe the object)
  
• Polygon data - the enclosed area is the key object (points connected by lines, using only the interior of these) - wards would be an example
  • Drawing polygons is tricky since 1) order matters, and 2) there may be more than one drawing (such as a donut hole exclusion)
    
• Raster (grid) data - data is associated with each cell in the grid (for example, satellites determining vegetation)

Key libraries are called without caching:

library(ggmap)
library(maps)
library(sp)
library(tmap)

## Warning: package 'tmap' was built under R version 3.2.5

## Warning: replacing previous import by 'tmaptools::%>%' when loading 'tmap'

And then, example code includes:

library(ggmap)
corvallis <- c(lon = -123.2620, lat = 44.5646)

# Get map at zoom level 5: map_5
map_5 <- get_map(corvallis, zoom = 5, scale = 1)

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=44.5646,-123.262&zoom=5&size=640x640&scale=1&maptype=terrain&language=en-EN&sensor=false

# Plot map at zoom level 5
ggmap(map_5)

# Get map at zoom level 13: corvallis_map
corvallis_map <- get_map(corvallis, zoom = 13, scale = 1)

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=44.5646,-123.262&zoom=13&size=640x640&scale=1&maptype=terrain&language=en-EN&sensor=false

# Plot map at zoom level 13
ggmap(corvallis_map)

sales_lon <- "-123.280349 ; -123.2330056 ; -123.263518 ; -123.259873 ; -123.263154 ; -123.284723 ; -123.236332 ; -123.266215 ; -123.2842478 ; -123.3094688 ; -123.260153 ; -123.292598 ; -123.2463352 ; -123.272113 ; -123.24968 ; -123.22785 ; -123.2759501 ; -123.300568 ; -123.230366 ; -123.274744"
sales_lat <- "44.578079 ; 44.5971776 ; 44.5692329 ; 44.594534 ; 44.536056 ; 44.598765 ; 44.603042 ; 44.593486 ; 44.5986974 ; 44.5519677 ; 44.595555 ; 44.594582 ; 44.5946242 ; 44.570883 ; 44.590523 ; 44.597 ; 44.5837454 ; 44.5546821 ; 44.590765 ; 44.572145"
sales_yrb <- "1967 ; 1990 ; 1948 ; 1978 ; 1979 ; 2002 ; 1972 ; 1970 ; 2002 ; 1959 ; 1998 ; 1971 ; 1998 ; 1938 ; 1963 ; 1972 ; 1967 ; 1952 ; 1965 ; 1926"
sales_beds <- "5 ; 3 ; 3 ; 0 ; 0 ; 3 ; 3 ; 3 ; 3 ; 3 ; 3 ; 5 ; 0 ; 6 ; 0 ; 4 ; 5 ; 3 ; 3 ; 3"
sales_prc <- "267500 ; 255000 ; 295000 ; 5000 ; 13950 ; 233000 ; 245000 ; 216000 ; 231000 ; 215000 ; 307000 ; 385000 ; 70000 ; 435350 ; 3000 ; 2e+05 ; 295900 ; 193000 ; 243000 ; 279900"
sales_finsq <- "1520 ; 1665 ; 1440 ; 784 ; 1344 ; 1567 ; 1174 ; 912 ; 1404 ; 1060 ; 1500 ; 1883 ; 1512 ; 2822 ; 540 ; 1855 ; 1960 ; 1316 ; 1872 ; 2278"
sales_class <- "Dwelling ; Dwelling ; Dwelling ; Mobile Home ; Mobile Home ; Dwelling ; Dwelling ; Dwelling ; Dwelling ; Dwelling ; Dwelling ; Dwelling ; Mobile Home ; Dwelling ; Mobile Home ; Dwelling ; Dwelling ; Dwelling ; Dwelling ; Dwelling"
sales_month <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 10, 8, 6, 4, 2, 1, 1)

sales <- data.frame(lon=as.numeric(strsplit(sales_lon, split=" ; ")[[1]]), 
                    lat=as.numeric(strsplit(sales_lat, split=" ; ")[[1]]), 
                    year_built=as.numeric(strsplit(sales_yrb, split=" ; ")[[1]]), 
                    bedrooms=as.numeric(strsplit(sales_beds, split=" ; ")[[1]]), 
                    price=as.numeric(strsplit(sales_prc, split=" ; ")[[1]]), 
                    finished_squarefeet=as.numeric(strsplit(sales_finsq, split=" ; ")[[1]]),
                    class=strsplit(sales_class, split=" ; ")[[1]],
                    month=as.integer(sales_month)
                    )

# Look at head() of sales
head(sales)

##         lon      lat year_built bedrooms  price finished_squarefeet
## 1 -123.2803 44.57808       1967        5 267500                1520
## 2 -123.2330 44.59718       1990        3 255000                1665
## 3 -123.2635 44.56923       1948        3 295000                1440
## 4 -123.2599 44.59453       1978        0   5000                 784
## 5 -123.2632 44.53606       1979        0  13950                1344
## 6 -123.2847 44.59877       2002        3 233000                1567
##         class month
## 1    Dwelling     1
## 2    Dwelling     2
## 3    Dwelling     3
## 4 Mobile Home     4
## 5 Mobile Home     5
## 6    Dwelling     6

# Swap out call to ggplot() with call to ggmap()
ggmap(corvallis_map) +
  geom_point(aes(lon, lat), data = sales)

# Map color to year_built
ggmap(corvallis_map) +
  geom_point(aes(lon, lat, color=year_built), data = sales)

# Map size to bedrooms
ggmap(corvallis_map) +
  geom_point(aes(lon, lat, size=bedrooms), data = sales)

# Map color to price / finished_squarefeet
ggmap(corvallis_map) +
  geom_point(aes(lon, lat, color=price/finished_squarefeet), data = sales)

corvallis <- c(lon = -123.2620, lat = 44.5646)

# Add a maptype argument to get a satellite map
corvallis_map_sat <- get_map(corvallis, zoom = 13, maptype="satellite")

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=44.5646,-123.262&zoom=13&size=640x640&scale=2&maptype=satellite&language=en-EN&sensor=false

# Edit to display satellite map
ggmap(corvallis_map_sat) +
  geom_point(aes(lon, lat, color = year_built), data = sales)

# Add source and maptype to get toner map from Stamen Maps
corvallis_map_bw <- get_map(corvallis, zoom = 13, maptype="toner", source="stamen")

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=44.5646,-123.262&zoom=13&size=640x640&scale=2&maptype=terrain&sensor=false

## Map from URL : http://tile.stamen.com/toner/13/1289/2959.png

## Map from URL : http://tile.stamen.com/toner/13/1290/2959.png

## Map from URL : http://tile.stamen.com/toner/13/1291/2959.png

## Map from URL : http://tile.stamen.com/toner/13/1292/2959.png

## Map from URL : http://tile.stamen.com/toner/13/1289/2960.png

## Map from URL : http://tile.stamen.com/toner/13/1290/2960.png

## Map from URL : http://tile.stamen.com/toner/13/1291/2960.png

## Map from URL : http://tile.stamen.com/toner/13/1292/2960.png

## Map from URL : http://tile.stamen.com/toner/13/1289/2961.png

## Map from URL : http://tile.stamen.com/toner/13/1290/2961.png

## Map from URL : http://tile.stamen.com/toner/13/1291/2961.png

## Map from URL : http://tile.stamen.com/toner/13/1292/2961.png

## Map from URL : http://tile.stamen.com/toner/13/1289/2962.png

## Map from URL : http://tile.stamen.com/toner/13/1290/2962.png

## Map from URL : http://tile.stamen.com/toner/13/1291/2962.png

## Map from URL : http://tile.stamen.com/toner/13/1292/2962.png

# Edit to display toner map
ggmap(corvallis_map_bw) +
  geom_point(aes(lon, lat, color = year_built), data = sales)

# Use base_layer argument to ggmap() to specify data and x, y mappings
ggmap(corvallis_map_bw, base_layer=ggplot(aes(lon, lat), data = sales)) +
  geom_point(aes(color = year_built))

# Use base_layer argument to ggmap() and add facet_wrap()
ggmap(corvallis_map_bw, base_layer=ggplot(aes(lon, lat), data = sales)) +
  geom_point(aes(color = class)) + 
  facet_wrap(~ class)

# Plot house sales using qmplot()
qmplot(lon, lat, data=sales, geom="point", color=bedrooms) + 
  facet_wrap(~ month)

## Using zoom = 13...

## Map from URL : http://tile.stamen.com/toner-lite/13/1289/2959.png

## Map from URL : http://tile.stamen.com/toner-lite/13/1290/2959.png

## Map from URL : http://tile.stamen.com/toner-lite/13/1291/2959.png

## Map from URL : http://tile.stamen.com/toner-lite/13/1289/2960.png

## Map from URL : http://tile.stamen.com/toner-lite/13/1290/2960.png

## Map from URL : http://tile.stamen.com/toner-lite/13/1291/2960.png

## Map from URL : http://tile.stamen.com/toner-lite/13/1289/2961.png

## Map from URL : http://tile.stamen.com/toner-lite/13/1290/2961.png

## Map from URL : http://tile.stamen.com/toner-lite/13/1291/2961.png

## Warning: `panel.margin` is deprecated. Please use `panel.spacing` property
## instead

## *** NEED TO COMMENT OUT (do not have ward_sales data file)
# Add a point layer with color mapped to ward
# ggplot(ward_sales, aes(lon, lat)) + 
#   geom_point(aes(color=ward))

# Add a point layer with color mapped to group
# ggplot(ward_sales, aes(lon, lat)) + 
#   geom_point(aes(color=group))

# Add a path layer with group mapped to group
# ggplot(ward_sales, aes(lon, lat)) + 
#   geom_path(aes(group=group))

# Add a polygon layer with fill mapped to ward, and group to group
# ggplot(ward_sales, aes(lon, lat)) + 
#   geom_polygon(aes(fill=ward, group=group))


# Fix the polygon cropping
# ggmap(corvallis_map_bw, 
#       base_layer = ggplot(ward_sales, aes(lon, lat)),
#       extent = "normal", maprange=FALSE
#       ) +
#   geom_polygon(aes(group = group, fill = ward))

# Repeat, but map fill to num_sales
# ggmap(corvallis_map_bw, 
#       base_layer = ggplot(ward_sales, aes(lon, lat)),
#       extent = "normal", maprange=FALSE
#       ) +
#   geom_polygon(aes(group = group, fill = num_sales))

# Repeat again, but map fill to avg_price
# ggmap(corvallis_map_bw, 
#       base_layer = ggplot(ward_sales, aes(lon, lat)),
#       extent = "normal", maprange=FALSE
#       ) +
#   geom_polygon(aes(group = group, fill = avg_price), alpha=0.8)


# Add a geom_point() layer
# ggplot(preds, aes(lon, lat)) + geom_point()

# Add a tile layer with fill mapped to predicted_price
# ggplot(preds, aes(lon, lat)) + geom_tile(aes(fill=predicted_price))

# Use ggmap() instead of ggplot()
# ggmap(corvallis_map_bw) + geom_tile(data=preds, aes(lon, lat, fill=predicted_price), alpha=0.8)

Chapter 2 - Point and Polygon Data

Introducing sp objects, preferred to data frames for spatial objects, which contain the CRS (coordinate reference system):

• The sp package allows for 1) classes for storing spatial data, and 2) methods for spatial objects/manipulation
  
• Useful for point, line, and polygon data; the sp data has become an R-standard, making it worth the overhead to learn

Introducing the sp and S4 objects (more details on the spdf from the exercises):

• The SpatialPolygons object is defined as having formal class ‘SpatialPolygons’ with n (4 in these examples) “slots”
  
• The SpatialPloygonsDataFrame will have n+1 (5 in these examples) “slots”, with the added “slot” being a data frame of the same length
  
• S4 is one of the object-oriented (OO) systems in R
  • class: defines a type of object, attributes, and relationships to other classes
    
  • methods: functions with behaviors that depend on class inputs
    
• Most of the more-common R functions are instead handles in S3 (frequently lists)
  • The S4 has “slots” rather than “lists”, and these can be accessed in a different manner than S3 access to “lists”
    
  • The “slots” can store anything (like lists) and can be recursive
    
• For accessing, a “slot”, there are multiple options
  • Dedicated method, such as proj4string(countries_sp)
    
  • The @ symbol, such as countries_sp@proj4string
    
  • The slot function, such as slot(countries_sp, “proj4string”)

Additional information on the sp class and methods:

• The hierarchy was that countries_sp has an @polygons “slot” which itself contains lists defining a country
  
• Drilling down further, each @Polygons has more lists, each containing @coords
  
• The benefit of all this complexity is allowing for a different polygon for all the objects (continental US, every single island, Alaska, etc.)
  
• The same concept can be extended for lines or points within sp (each is a class)
  
• Generally, the subsetting is the same as for S3 (more common) objects

Introduction to tmap, which is very similar in concept to ggplot2:

• Plots are built in layers, though it expects to receive a spatial object
  
• The first call is typically to tm_shape(), which calls the data but does not really do much to it
  
• Can then add layers such as tm_borders() or tm_fill(col=“part”) or tm_compass() or tmap_style(“cobalt”)
  • The variable names in tmap need to be quoted – key difference from ggplot
    
• Differences from ggplot include: 1) there are no scale_ calls (happen automatically), 2) can have many tm_shape() in a plot for layering, 3) no explicit x/y aesthetic needed, 4) variable names must be quoted

Example code includes:

# Create the countries_sp dataset
library(maps)
origCountry <- maps::map("world", fill = TRUE)

IDs <- sapply(strsplit(origCountry$names, ":"), function(x) x[1])
countries_sp <- maptools::map2SpatialPolygons(origCountry, IDs=IDs, 
                                              proj4string=CRS("+proj=longlat +datum=WGS84")
                                              )

library(sp)

# Print countries_sp (skip this, it looks like a pile of vomit!)
# print(countries_sp)

# Call summary() on countries_sp
summary(countries_sp)

## Object of class SpatialPolygons
## Coordinates:
##          min       max
## x -180.00000 190.27084
## y  -85.19218  83.59961
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0]

# Call plot() on countries_sp
plot(countries_sp)


# Call str() on countries_sp (skip this, it is a wreck, the below is much more user-friendly)
# str(countries_sp)

# Call str() on countries_sp with max.level = 2
str(countries_sp, max.level=2)

## Formal class 'SpatialPolygons' [package "sp"] with 4 slots
##   ..@ polygons   :List of 253
##   .. .. [list output truncated]
##   ..@ plotOrder  : int [1:253] 8 184 40 241 48 33 14 86 114 10 ...
##   ..@ bbox       : num [1:2, 1:2] -180 -85.2 190.3 83.6
##   .. ..- attr(*, "dimnames")=List of 2
##   ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot

## *** MAY NEED TO SKIP UNLESS CAN FIND/CREATE THE spdf ["name", "iso_a3", "population", "gdp", "region", "subregion"]

ctry_db <- data.frame(ctr=1:length(countries_sp@polygons), 
                      name=NA, iso_a3=NA, population=NA, 
                      gdp=NA, region=NA, subregion=NA
                      )

for (intCtr in 1:nrow(ctry_db)) { 
    ctry_db$name[intCtr] <- countries_sp@polygons[[intCtr]]@ID 
}

a3List <- "AFG\nALB\nDZA\nASM\nAND\nAGO\nAIA\nATA\nATG\nARG\nARM\nABW\n#N/A\nAUS\nAUT\nAZE\n#N/A\nBHS\nBHR\nBGD\nBRB\n#N/A\nBLR\nBEL\nBLZ\nBEN\nBMU\nBTN\nBOL\n#N/A\nBIH\nBWA\nBRA\nBRN\nBGR\nBFA\nBDI\nKHM\nCMR\nCAN\n#N/A\nCPV\nCYM\nCAF\nTCD\n#N/A\nCHL\nCHN\nCXR\nCCK\nCOL\nCOM\nCOK\nCRI\nHRV\nCUB\nCUW\nCYP\nCZE\nCOD\nDNK\nDJI\nDMA\nDOM\nECU\nEGY\nSLV\nGNQ\nERI\nEST\nETH\nFLK\nFRO\nFJI\nFIN\nFRA\nGUF\nPYF\nATF\nGAB\nGMB\nGEO\nDEU\nGHA\nGRC\nGRL\nGRD\n#N/A\nGLP\nGUM\nGTM\nGGY\nGIN\nGNB\nGUY\nHTI\nHMD\nHND\nHUN\nISL\nIND\nIDN\nIRN\nIRQ\nIRL\nIMN\nISR\nITA\nCIV\nJAM\nJPN\nJEY\nJOR\nKAZ\nKEN\nKIR\n#N/A\nKWT\nKGZ\nLAO\nLVA\nLBN\nLSO\nLBR\nLBY\nLIE\nLTU\nLUX\nMKD\nMDG\n#N/A\nMWI\nMYS\nMDV\nMLI\nMLT\nMHL\nMTQ\nMRT\nMUS\nMYT\nMEX\nFSM\nMDA\nMCO\nMNG\nMNE\nMSR\nMAR\nMOZ\nMMR\nNAM\nNRU\nNPL\nNLD\n#N/A\nNCL\nNZL\nNIC\nNER\nNGA\nNIU\nNFK\nPRK\nMNP\nNOR\nOMN\nPAK\nPLW\nPSE\nPAN\nPNG\nPRY\nPER\nPHL\nPCN\nPOL\nPRT\nPRI\nQAT\nCOG\nREU\nROU\nRUS\nRWA\nBES\nBLM\nSHN\nKNA\nLCA\nMAF\nSPM\nVCT\nWSM\nSMR\nSTP\nSAU\nSEN\nSRB\nSYC\n#N/A\nSLE\nSGP\n#N/A\nSXM\nSVK\nSVN\nSLB\nSOM\nZAF\nSGS\nKOR\n#N/A\nSSD\nESP\nLKA\nSDN\nSUR\nSWZ\nSWE\nCHE\nSYR\nTWN\nTJK\nTZA\nTHA\nTLS\n#N/A\nTGO\nTON\nTTO\nTUN\nTUR\nTKM\nTCA\nUGA\nGBR\nUKR\nARE\nURY\nUSA\nUZB\nVUT\nVAT\nVEN\nVNM\nVGB\nVIR\nWLF\nESH\nYEM\nZMB\nZWE\n"

a3Split <- strsplit(a3List, "\n")[[1]]
a3Split[a3Split == "#N/A"] <- NA

ctry_db$iso_a3 <- a3Split


popList <- "33369945\n2903700\n40375954\n55602\n69165\n25830958\n14763\n#N/A\n92738\n43847277\n3026048\n104263\n#N/A\n24309330\n8569633\n9868447\n#N/A\n392718\n1396829\n162910864\n285006\n#N/A\n9481521\n11371928\n366942\n11166658\n61662\n784103\n10888402\n#N/A\n3802134\n2303820\n209567920\n428874\n7097796\n18633725\n11552561\n15827241\n23924407\n36286378\n#N/A\n526993\n60764\n4998493\n14496739\n#N/A\n18131850\n1382323332\n#N/A\n#N/A\n48654392\n807118\n20948\n4857218\n4225001\n11392889\n158635\n1176598\n10548058\n79722624\n5690750\n899598\n73016\n10648613\n16385450\n93383574\n6146419\n869587\n5351680\n1309104\n101853268\n2912\n48239\n897537\n5523904\n64668129\n275688\n285735\n#N/A\n1763142\n2054986\n3979781\n80682351\n28033375\n10919459\n56196\n107327\n#N/A\n470547\n172094\n16672956\n164466\n12947122\n1888429\n770610\n10848175\n#N/A\n8189501\n9821318\n331778\n1326801576\n260581100\n80043146\n37547686\n4713993\n88421\n8192463\n59801004\n23254184\n2803362\n126323715\n#N/A\n7747800\n17855384\n47251449\n114405\n#N/A\n4007146\n6033769\n6918367\n1955742\n5988153\n2160309\n4615222\n6330159\n37776\n2850030\n576243\n2081012\n24915822\n#N/A\n17749826\n30751602\n369812\n18134835\n419615\n53069\n396364\n4166463\n1277459\n246496\n128632004\n#N/A\n4062862\n37863\n3006444\n626101\n5154\n34817065\n28751362\n54363426\n2513981\n10263\n28850717\n16979729\n#N/A\n266431\n4565185\n6150035\n20715285\n186987563\n1612\n#N/A\n25281327\n55389\n5271958\n4654471\n192826502\n21501\n4797239\n3990406\n7776115\n6725430\n31774225\n102250133\n#N/A\n38593161\n10304434\n3680772\n2291368\n4740992\n867214\n19372734\n143439832\n11882766\n#N/A\n#N/A\n3956\n56183\n186383\n#N/A\n6301\n109644\n194523\n31950\n194390\n32157974\n15589485\n8812705\n97026\n#N/A\n6592102\n5696506\n#N/A\n39538\n5429418\n2069362\n594934\n11079013\n54978907\n#N/A\n50503933\n#N/A\n12733427\n46064604\n20810816\n41175541\n547610\n1304063\n9851852\n8379477\n18563595\n23395600\n8669464\n55155473\n68146609\n1211245\n#N/A\n7496833\n106915\n1364973\n11375220\n79622062\n5438670\n34904\n40322768\n65111143\n44624373\n9266971\n3444071\n324118787\n30300446\n270470\n801\n31518855\n94444200\n30659\n106415\n13112\n584206\n27477600\n16717332\n15966810\n"

popSplit <- strsplit(popList, "\n")[[1]]
popSplit[popSplit == "#N/A"] <- NA

ctry_db$population <- as.numeric(popSplit)


rgnList <- "Asia\nEurope\nAfrica\nOceania\nEurope\nAfrica\nAmericas\n#N/A\nAmericas\nAmericas\nAsia\nAmericas\n#N/A\nOceania\nEurope\nAsia\n#N/A\nAmericas\nAsia\nAsia\nAmericas\n#N/A\nEurope\nEurope\nAmericas\nAfrica\nAmericas\nAsia\nAmericas\n#N/A\nEurope\nAfrica\nAmericas\nAsia\nEurope\nAfrica\nAfrica\nAsia\nAfrica\nAmericas\n#N/A\nAfrica\nAmericas\nAfrica\nAfrica\n#N/A\nAmericas\nAsia\n#N/A\n#N/A\nAmericas\nAfrica\nOceania\nAmericas\nEurope\nAmericas\nAmericas\nEurope\nEurope\nAfrica\nEurope\nAfrica\nAmericas\nAmericas\nAmericas\nAfrica\nAmericas\nAfrica\nAfrica\nEurope\nAfrica\nAmericas\nEurope\nOceania\nEurope\nEurope\nAmericas\nOceania\n#N/A\nAfrica\nAfrica\nAsia\nEurope\nAfrica\nEurope\nAmericas\nAmericas\n#N/A\nAmericas\nOceania\nAmericas\nEurope\nAfrica\nAfrica\nAmericas\nAmericas\n#N/A\nAmericas\nEurope\nEurope\nAsia\nAsia\nAsia\nAsia\nEurope\nEurope\nAsia\nEurope\nAfrica\nAmericas\nAsia\n#N/A\nAsia\nAsia\nAfrica\nOceania\n#N/A\nAsia\nAsia\nAsia\nEurope\nAsia\nAfrica\nAfrica\nAfrica\nEurope\nEurope\nEurope\nEurope\nAfrica\n#N/A\nAfrica\nAsia\nAsia\nAfrica\nEurope\nOceania\nAmericas\nAfrica\nAfrica\nAfrica\nAmericas\n#N/A\nEurope\nEurope\nAsia\nEurope\nAmericas\nAfrica\nAfrica\nAsia\nAfrica\nOceania\nAsia\nEurope\n#N/A\nOceania\nOceania\nAmericas\nAfrica\nAfrica\nOceania\n#N/A\nAsia\nOceania\nEurope\nAsia\nAsia\nOceania\nAsia\nAmericas\nOceania\nAmericas\nAmericas\nAsia\n#N/A\nEurope\nEurope\nAmericas\nAsia\nAfrica\nAfrica\nEurope\nEurope\nAfrica\n#N/A\n#N/A\nAfrica\nAmericas\nAmericas\n#N/A\nAmericas\nAmericas\nOceania\nEurope\nAfrica\nAsia\nAfrica\nEurope\nAfrica\n#N/A\nAfrica\nAsia\n#N/A\nAmericas\nEurope\nEurope\nOceania\nAfrica\nAfrica\n#N/A\nAsia\n#N/A\nAfrica\nEurope\nAsia\nAfrica\nAmericas\nAfrica\nEurope\nEurope\nAsia\nAsia\nAsia\nAfrica\nAsia\nAsia\n#N/A\nAfrica\nOceania\nAmericas\nAfrica\nAsia\nAsia\nAmericas\nAfrica\nEurope\nEurope\nAsia\nAmericas\nAmericas\nAsia\nOceania\nEurope\nAmericas\nAsia\nAmericas\nAmericas\nOceania\nAfrica\nAsia\nAfrica\nAfrica\n"

rgnSplit <- strsplit(rgnList, "\n")[[1]]
rgnSplit[rgnSplit == "#N/A"] <- NA

ctry_db$region <- rgnSplit


subList <- "Southern Asia\nSouthern Europe\nNorthern Africa\nPolynesia\nSouthern Europe\nMiddle Africa\nCaribbean\n#N/A\nCaribbean\nSouth America\nWestern Asia\nCaribbean\n#N/A\nAustralia and New Zealand\nWestern Europe\nWestern Asia\n#N/A\nCaribbean\nWestern Asia\nSouthern Asia\nCaribbean\n#N/A\nEastern Europe\nWestern Europe\nCentral America\nWestern Africa\nNorthern America\nSouthern Asia\nSouth America\n#N/A\nSouthern Europe\nSouthern Africa\nSouth America\nSouth-Eastern Asia\nEastern Europe\nWestern Africa\nEastern Africa\nSouth-Eastern Asia\nMiddle Africa\nNorthern America\n#N/A\nWestern Africa\nCaribbean\nMiddle Africa\nMiddle Africa\n#N/A\nSouth America\nEastern Asia\n#N/A\n#N/A\nSouth America\nEastern Africa\nPolynesia\nCentral America\nSouthern Europe\nCaribbean\nCaribbean\nSouthern Europe\nEastern Europe\nMiddle Africa\nNorthern Europe\nEastern Africa\nCaribbean\nCaribbean\nSouth America\nNorthern Africa\nCentral America\nMiddle Africa\nEastern Africa\nNorthern Europe\nEastern Africa\nSouth America\nNorthern Europe\nMelanesia\nNorthern Europe\nWestern Europe\nSouth America\nPolynesia\n#N/A\nMiddle Africa\nWestern Africa\nWestern Asia\nWestern Europe\nWestern Africa\nSouthern Europe\nNorthern America\nCaribbean\n#N/A\nCaribbean\nMicronesia\nCentral America\nNorthern Europe\nWestern Africa\nWestern Africa\nSouth America\nCaribbean\n#N/A\nCentral America\nEastern Europe\nNorthern Europe\nSouthern Asia\nSouth-Eastern Asia\nSouthern Asia\nWestern Asia\nNorthern Europe\nNorthern Europe\nWestern Asia\nSouthern Europe\nWestern Africa\nCaribbean\nEastern Asia\n#N/A\nWestern Asia\nCentral Asia\nEastern Africa\nMicronesia\n#N/A\nWestern Asia\nCentral Asia\nSouth-Eastern Asia\nNorthern Europe\nWestern Asia\nSouthern Africa\nWestern Africa\nNorthern Africa\nWestern Europe\nNorthern Europe\nWestern Europe\nSouthern Europe\nEastern Africa\n#N/A\nEastern Africa\nSouth-Eastern Asia\nSouthern Asia\nWestern Africa\nSouthern Europe\nMicronesia\nCaribbean\nWestern Africa\nEastern Africa\nEastern Africa\nCentral America\n#N/A\nEastern Europe\nWestern Europe\nEastern Asia\nSouthern Europe\nCaribbean\nNorthern Africa\nEastern Africa\nSouth-Eastern Asia\nSouthern Africa\nMicronesia\nSouthern Asia\nWestern Europe\n#N/A\nMelanesia\nAustralia and New Zealand\nCentral America\nWestern Africa\nWestern Africa\nPolynesia\n#N/A\nEastern Asia\nMicronesia\nNorthern Europe\nWestern Asia\nSouthern Asia\nMicronesia\nWestern Asia\nCentral America\nMelanesia\nSouth America\nSouth America\nSouth-Eastern Asia\n#N/A\nEastern Europe\nSouthern Europe\nCaribbean\nWestern Asia\nMiddle Africa\nEastern Africa\nEastern Europe\nEastern Europe\nEastern Africa\n#N/A\n#N/A\nWestern Africa\nCaribbean\nCaribbean\n#N/A\nNorthern America\nCaribbean\nPolynesia\nSouthern Europe\nMiddle Africa\nWestern Asia\nWestern Africa\nSouthern Europe\nEastern Africa\n#N/A\nWestern Africa\nSouth-Eastern Asia\n#N/A\nCaribbean\nEastern Europe\nSouthern Europe\nMelanesia\nEastern Africa\nSouthern Africa\n#N/A\nEastern Asia\n#N/A\nEastern Africa\nSouthern Europe\nSouthern Asia\nNorthern Africa\nSouth America\nSouthern Africa\nNorthern Europe\nWestern Europe\nWestern Asia\nEastern Asia\nCentral Asia\nEastern Africa\nSouth-Eastern Asia\nSouth-Eastern Asia\n#N/A\nWestern Africa\nPolynesia\nCaribbean\nNorthern Africa\nWestern Asia\nCentral Asia\nCaribbean\nEastern Africa\nNorthern Europe\nEastern Europe\nWestern Asia\nSouth America\nNorthern America\nCentral Asia\nMelanesia\nSouthern Europe\nSouth America\nSouth-Eastern Asia\nCaribbean\nCaribbean\nPolynesia\nNorthern Africa\nWestern Asia\nEastern Africa\nEastern Africa\n"

subSplit <- strsplit(subList, "\n")[[1]]
subSplit[subSplit == "#N/A"] <- NA

ctry_db$subregion <- subSplit


gdpList <- "18395\n12144\n168318\n#N/A\n#N/A\n91939\n#N/A\n#N/A\n1303\n541748\n10754\n#N/A\n#N/A\n1256640\n387299\n35686\n#N/A\n9047\n31823\n226760\n4473\n#N/A\n48126\n470179\n1770\n8930\n#N/A\n2085\n35699\n#N/A\n16532\n10948\n1769601\n10458\n50446\n12006\n2742\n19368\n30870\n1532343\n#N/A\n1684\n#N/A\n1782\n10441\n#N/A\n234903\n11391619\n#N/A\n#N/A\n274135\n622\n#N/A\n57689\n49855\n#N/A\n#N/A\n19931\n193535\n39820\n302571\n1894\n524\n71457\n99118\n346565\n26610\n11638\n5352\n23476\n69218\n#N/A\n#N/A\n4556\n239186\n2488284\n#N/A\n#N/A\n#N/A\n14563\n886\n14463\n3494898\n42761\n195878\n#N/A\n1028\n#N/A\n#N/A\n#N/A\n68389\n#N/A\n6754\n1168\n3456\n8259\n#N/A\n20930\n117065\n19444\n2250987\n940953\n412304\n156323\n307917\n#N/A\n311739\n1852499\n34649\n13779\n4730300\n#N/A\n39453\n128109\n69170\n166\n6560\n110455\n5794\n13761\n27945\n51815\n1806\n2168\n39389\n#N/A\n42776\n60984\n10492\n9740\n#N/A\n5474\n302748\n3270\n14103\n10463\n188\n#N/A\n4718\n11740\n#N/A\n1063606\n325\n6650\n#N/A\n11164\n4242\n#N/A\n104908\n12045\n68277\n10183\n#N/A\n21154\n769930\n#N/A\n#N/A\n179359\n13413\n7566\n415080\n#N/A\n#N/A\n#N/A\n#N/A\n376268\n59675\n284519\n296\n#N/A\n55227\n19915\n27323\n180291\n311687\n#N/A\n467350\n205860\n100852\n156595\n8834\n#N/A\n186514\n1267754\n8341\n#N/A\n#N/A\n#N/A\n955\n1439\n#N/A\n#N/A\n766\n876\n1556\n351\n637785\n14870\n37755\n1419\n#N/A\n4289\n296642\n#N/A\n#N/A\n90263\n44122\n1218\n#N/A\n280367\n#N/A\n1404383\n#N/A\n2628\n1252163\n82239\n94297\n4137\n3430\n517440\n662483\n#VALUE!\n519149\n6612\n46695\n390592\n2501\n#N/A\n4520\n430\n22809\n42388\n735716\n36573\n#N/A\n25613\n2649893\n87198\n375022\n54374\n18561934\n66797\n773\n#N/A\n333715\n200493\n#N/A\n#N/A\n#N/A\n#N/A\n31326\n20574\n14193\n"

gdpSplit <- strsplit(gdpList, "\n")[[1]]
gdpSplit[gdpSplit == "#N/A"] <- NA

ctry_db$gdp <- as.numeric(gdpSplit)

## Warning: NAs introduced by coercion

# Create the spdf file
rownames(ctry_db) <- ctry_db$name
countries_spdf <- SpatialPolygonsDataFrame(countries_sp, data=ctry_db)

# Call summary() on countries_spdf and countries_sp
summary(countries_sp)

## Object of class SpatialPolygons
## Coordinates:
##          min       max
## x -180.00000 190.27084
## y  -85.19218  83.59961
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0]

summary(countries_spdf)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##          min       max
## x -180.00000 190.27084
## y  -85.19218  83.59961
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0]
## Data attributes:
##       ctr        name                                      iso_a3   
##  Min.   :  1   NULL:Afghanistan                           NULL:AFG  
##  1st Qu.: 64   NULL:Albania                               NULL:ALB  
##  Median :127   NULL:Algeria                               NULL:DZA  
##  Mean   :127   NULL:American Samoa                        NULL:ASM  
##  3rd Qu.:190   NULL:Andorra                               NULL:AND  
##  Max.   :253   NULL:Angola                                NULL:AGO  
##                NULL:Anguilla                              NULL:AIA  
##                NULL:Antarctica                            NULL:ATA  
##                NULL:Antigua                               NULL:ATG  
##                NULL:Argentina                             NULL:ARG  
##                NULL:Armenia                               NULL:ARM  
##                NULL:Aruba                                 NULL:ABW  
##                NULL:Ascension Island                      NULL:NA   
##                NULL:Australia                             NULL:AUS  
##                NULL:Austria                               NULL:AUT  
##                NULL:Azerbaijan                            NULL:AZE  
##                NULL:Azores                                NULL:NA   
##                NULL:Bahamas                               NULL:BHS  
##                NULL:Bahrain                               NULL:BHR  
##                NULL:Bangladesh                            NULL:BGD  
##                NULL:Barbados                              NULL:BRB  
##                NULL:Barbuda                               NULL:NA   
##                NULL:Belarus                               NULL:BLR  
##                NULL:Belgium                               NULL:BEL  
##                NULL:Belize                                NULL:BLZ  
##                NULL:Benin                                 NULL:BEN  
##                NULL:Bermuda                               NULL:BMU  
##                NULL:Bhutan                                NULL:BTN  
##                NULL:Bolivia                               NULL:BOL  
##                NULL:Bonaire                               NULL:NA   
##                NULL:Bosnia and Herzegovina                NULL:BIH  
##                NULL:Botswana                              NULL:BWA  
##                NULL:Brazil                                NULL:BRA  
##                NULL:Brunei                                NULL:BRN  
##                NULL:Bulgaria                              NULL:BGR  
##                NULL:Burkina Faso                          NULL:BFA  
##                NULL:Burundi                               NULL:BDI  
##                NULL:Cambodia                              NULL:KHM  
##                NULL:Cameroon                              NULL:CMR  
##                NULL:Canada                                NULL:CAN  
##                NULL:Canary Islands                        NULL:NA   
##                NULL:Cape Verde                            NULL:CPV  
##                NULL:Cayman Islands                        NULL:CYM  
##                NULL:Central African Republic              NULL:CAF  
##                NULL:Chad                                  NULL:TCD  
##                NULL:Chagos Archipelago                    NULL:NA   
##                NULL:Chile                                 NULL:CHL  
##                NULL:China                                 NULL:CHN  
##                NULL:Christmas Island                      NULL:CXR  
##                NULL:Cocos Islands                         NULL:CCK  
##                NULL:Colombia                              NULL:COL  
##                NULL:Comoros                               NULL:COM  
##                NULL:Cook Islands                          NULL:COK  
##                NULL:Costa Rica                            NULL:CRI  
##                NULL:Croatia                               NULL:HRV  
##                NULL:Cuba                                  NULL:CUB  
##                NULL:Curacao                               NULL:CUW  
##                NULL:Cyprus                                NULL:CYP  
##                NULL:Czech Republic                        NULL:CZE  
##                NULL:Democratic Republic of the Congo      NULL:COD  
##                NULL:Denmark                               NULL:DNK  
##                NULL:Djibouti                              NULL:DJI  
##                NULL:Dominica                              NULL:DMA  
##                NULL:Dominican Republic                    NULL:DOM  
##                NULL:Ecuador                               NULL:ECU  
##                NULL:Egypt                                 NULL:EGY  
##                NULL:El Salvador                           NULL:SLV  
##                NULL:Equatorial Guinea                     NULL:GNQ  
##                NULL:Eritrea                               NULL:ERI  
##                NULL:Estonia                               NULL:EST  
##                NULL:Ethiopia                              NULL:ETH  
##                NULL:Falkland Islands                      NULL:FLK  
##                NULL:Faroe Islands                         NULL:FRO  
##                NULL:Fiji                                  NULL:FJI  
##                NULL:Finland                               NULL:FIN  
##                NULL:France                                NULL:FRA  
##                NULL:French Guiana                         NULL:GUF  
##                NULL:French Polynesia                      NULL:PYF  
##                NULL:French Southern and Antarctic Lands   NULL:ATF  
##                NULL:Gabon                                 NULL:GAB  
##                NULL:Gambia                                NULL:GMB  
##                NULL:Georgia                               NULL:GEO  
##                NULL:Germany                               NULL:DEU  
##                NULL:Ghana                                 NULL:GHA  
##                NULL:Greece                                NULL:GRC  
##                NULL:Greenland                             NULL:GRL  
##                NULL:Grenada                               NULL:GRD  
##                NULL:Grenadines                            NULL:NA   
##                NULL:Guadeloupe                            NULL:GLP  
##                NULL:Guam                                  NULL:GUM  
##                NULL:Guatemala                             NULL:GTM  
##                NULL:Guernsey                              NULL:GGY  
##                NULL:Guinea                                NULL:GIN  
##                NULL:Guinea-Bissau                         NULL:GNB  
##                NULL:Guyana                                NULL:GUY  
##                NULL:Haiti                                 NULL:HTI  
##                NULL:Heard Island                          NULL:HMD  
##                NULL:Honduras                              NULL:HND  
##                NULL:Hungary                               NULL:HUN  
##                NULL:Iceland                               NULL:ISL  
##                NULL:India                                 NULL:IND  
##                NULL:Indonesia                             NULL:IDN  
##                NULL:Iran                                  NULL:IRN  
##                NULL:Iraq                                  NULL:IRQ  
##                NULL:Ireland                               NULL:IRL  
##                NULL:Isle of Man                           NULL:IMN  
##                NULL:Israel                                NULL:ISR  
##                NULL:Italy                                 NULL:ITA  
##                NULL:Ivory Coast                           NULL:CIV  
##                NULL:Jamaica                               NULL:JAM  
##                NULL:Japan                                 NULL:JPN  
##                NULL:Jersey                                NULL:JEY  
##                NULL:Jordan                                NULL:JOR  
##                NULL:Kazakhstan                            NULL:KAZ  
##                NULL:Kenya                                 NULL:KEN  
##                NULL:Kiribati                              NULL:KIR  
##                NULL:Kosovo                                NULL:NA   
##                NULL:Kuwait                                NULL:KWT  
##                NULL:Kyrgyzstan                            NULL:KGZ  
##                NULL:Laos                                  NULL:LAO  
##                NULL:Latvia                                NULL:LVA  
##                NULL:Lebanon                               NULL:LBN  
##                NULL:Lesotho                               NULL:LSO  
##                NULL:Liberia                               NULL:LBR  
##                NULL:Libya                                 NULL:LBY  
##                NULL:Liechtenstein                         NULL:LIE  
##                NULL:Lithuania                             NULL:LTU  
##                NULL:Luxembourg                            NULL:LUX  
##                NULL:Macedonia                             NULL:MKD  
##                NULL:Madagascar                            NULL:MDG  
##                NULL:Madeira Islands                       NULL:NA   
##                NULL:Malawi                                NULL:MWI  
##                NULL:Malaysia                              NULL:MYS  
##                NULL:Maldives                              NULL:MDV  
##                NULL:Mali                                  NULL:MLI  
##                NULL:Malta                                 NULL:MLT  
##                NULL:Marshall Islands                      NULL:MHL  
##                NULL:Martinique                            NULL:MTQ  
##                NULL:Mauritania                            NULL:MRT  
##                NULL:Mauritius                             NULL:MUS  
##                NULL:Mayotte                               NULL:MYT  
##                NULL:Mexico                                NULL:MEX  
##                NULL:Micronesia                            NULL:FSM  
##                NULL:Moldova                               NULL:MDA  
##                NULL:Monaco                                NULL:MCO  
##                NULL:Mongolia                              NULL:MNG  
##                NULL:Montenegro                            NULL:MNE  
##                NULL:Montserrat                            NULL:MSR  
##                NULL:Morocco                               NULL:MAR  
##                NULL:Mozambique                            NULL:MOZ  
##                NULL:Myanmar                               NULL:MMR  
##                NULL:Namibia                               NULL:NAM  
##                NULL:Nauru                                 NULL:NRU  
##                NULL:Nepal                                 NULL:NPL  
##                NULL:Netherlands                           NULL:NLD  
##                NULL:Nevis                                 NULL:NA   
##                NULL:New Caledonia                         NULL:NCL  
##                NULL:New Zealand                           NULL:NZL  
##                NULL:Nicaragua                             NULL:NIC  
##                NULL:Niger                                 NULL:NER  
##                NULL:Nigeria                               NULL:NGA  
##                NULL:Niue                                  NULL:NIU  
##                NULL:Norfolk Island                        NULL:NFK  
##                NULL:North Korea                           NULL:PRK  
##                NULL:Northern Mariana Islands              NULL:MNP  
##                NULL:Norway                                NULL:NOR  
##                NULL:Oman                                  NULL:OMN  
##                NULL:Pakistan                              NULL:PAK  
##                NULL:Palau                                 NULL:PLW  
##                NULL:Palestine                             NULL:PSE  
##                NULL:Panama                                NULL:PAN  
##                NULL:Papua New Guinea                      NULL:PNG  
##                NULL:Paraguay                              NULL:PRY  
##                NULL:Peru                                  NULL:PER  
##                NULL:Philippines                           NULL:PHL  
##                NULL:Pitcairn Islands                      NULL:PCN  
##                NULL:Poland                                NULL:POL  
##                NULL:Portugal                              NULL:PRT  
##                NULL:Puerto Rico                           NULL:PRI  
##                NULL:Qatar                                 NULL:QAT  
##                NULL:Republic of Congo                     NULL:COG  
##                NULL:Reunion                               NULL:REU  
##                NULL:Romania                               NULL:ROU  
##                NULL:Russia                                NULL:RUS  
##                NULL:Rwanda                                NULL:RWA  
##                NULL:Saba                                  NULL:BES  
##                NULL:Saint Barthelemy                      NULL:BLM  
##                NULL:Saint Helena                          NULL:SHN  
##                NULL:Saint Kitts                           NULL:KNA  
##                NULL:Saint Lucia                           NULL:LCA  
##                NULL:Saint Martin                          NULL:MAF  
##                NULL:Saint Pierre and Miquelon             NULL:SPM  
##                NULL:Saint Vincent                         NULL:VCT  
##                NULL:Samoa                                 NULL:WSM  
##                NULL:San Marino                            NULL:SMR  
##                NULL:Sao Tome and Principe                 NULL:STP  
##                NULL:Saudi Arabia                          NULL:SAU  
##                NULL:Senegal                               NULL:SEN  
##                NULL:Serbia                                NULL:SRB  
##                NULL:Seychelles                            NULL:SYC  
##                NULL:Siachen Glacier                       NULL:NA   
##                NULL:Sierra Leone                          NULL:SLE  
##                NULL:Singapore                             NULL:SGP  
##                NULL:Sint Eustatius                        NULL:NA   
##                NULL:Sint Maarten                          NULL:SXM  
##                NULL:Slovakia                              NULL:SVK  
##                NULL:Slovenia                              NULL:SVN  
##                NULL:Solomon Islands                       NULL:SLB  
##                NULL:Somalia                               NULL:SOM  
##                NULL:South Africa                          NULL:ZAF  
##                NULL:South Georgia                         NULL:SGS  
##                NULL:South Korea                           NULL:KOR  
##                NULL:South Sandwich Islands                NULL:NA   
##                NULL:South Sudan                           NULL:SSD  
##                NULL:Spain                                 NULL:ESP  
##                NULL:Sri Lanka                             NULL:LKA  
##                NULL:Sudan                                 NULL:SDN  
##                NULL:Suriname                              NULL:SUR  
##                NULL:Swaziland                             NULL:SWZ  
##                NULL:Sweden                                NULL:SWE  
##                NULL:Switzerland                           NULL:CHE  
##                NULL:Syria                                 NULL:SYR  
##                NULL:Taiwan                                NULL:TWN  
##                NULL:Tajikistan                            NULL:TJK  
##                NULL:Tanzania                              NULL:TZA  
##                NULL:Thailand                              NULL:THA  
##                NULL:Timor-Leste                           NULL:TLS  
##                NULL:Tobago                                NULL:NA   
##                NULL:Togo                                  NULL:TGO  
##                NULL:Tonga                                 NULL:TON  
##                NULL:Trinidad                              NULL:TTO  
##                NULL:Tunisia                               NULL:TUN  
##                NULL:Turkey                                NULL:TUR  
##                NULL:Turkmenistan                          NULL:TKM  
##                NULL:Turks and Caicos Islands              NULL:TCA  
##                NULL:Uganda                                NULL:UGA  
##                NULL:UK                                    NULL:GBR  
##                NULL:Ukraine                               NULL:UKR  
##                NULL:United Arab Emirates                  NULL:ARE  
##                NULL:Uruguay                               NULL:URY  
##                NULL:USA                                   NULL:USA  
##                NULL:Uzbekistan                            NULL:UZB  
##                NULL:Vanuatu                               NULL:VUT  
##                NULL:Vatican                               NULL:VAT  
##                NULL:Venezuela                             NULL:VEN  
##                NULL:Vietnam                               NULL:VNM  
##                NULL:Virgin Islands, British               NULL:VGB  
##                NULL:Virgin Islands, US                    NULL:VIR  
##                NULL:Wallis and Futuna                     NULL:WLF  
##                NULL:Western Sahara                        NULL:ESH  
##                NULL:Yemen                                 NULL:YEM  
##                NULL:Zambia                                NULL:ZMB  
##                NULL:Zimbabwe                              NULL:ZWE  
##    population             gdp            region        
##  Min.   :8.010e+02   Min.   :     166   NULL:Asia      
##  1st Qu.:5.321e+05   1st Qu.:    7160   NULL:Europe    
##  Median :5.607e+06   Median :   34649   NULL:Africa    
##  Mean   :3.285e+07   Mean   :  400281   NULL:Oceania   
##  3rd Qu.:2.079e+07   3rd Qu.:  198186   NULL:Europe    
##  Max.   :1.382e+09   Max.   :18561934   NULL:Africa    
##  NA's   :27          NA's   :66         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Oceania   
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:NA        
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:NA        
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:NA        
##                                         NULL:NA        
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Oceania   
##                                         NULL:Americas  
##                                         NULL:Europe    
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Europe    
##                                         NULL:Oceania   
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Americas  
##                                         NULL:Oceania   
##                                         NULL:NA        
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Americas  
##                                         NULL:Oceania   
##                                         NULL:Americas  
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Americas  
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:NA        
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Oceania   
##                                         NULL:NA        
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:NA        
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Oceania   
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:Europe    
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Oceania   
##                                         NULL:Asia      
##                                         NULL:Europe    
##                                         NULL:NA        
##                                         NULL:Oceania   
##                                         NULL:Oceania   
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Oceania   
##                                         NULL:NA        
##                                         NULL:Asia      
##                                         NULL:Oceania   
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Oceania   
##                                         NULL:Asia      
##                                         NULL:Americas  
##                                         NULL:Oceania   
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:NA        
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:NA        
##                                         NULL:NA        
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:NA        
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:Oceania   
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Africa    
##                                         NULL:NA        
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:NA        
##                                         NULL:Americas  
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Oceania   
##                                         NULL:Africa    
##                                         NULL:Africa    
##                                         NULL:NA        
##                                         NULL:Asia      
##                                         NULL:NA        
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:NA        
##                                         NULL:Africa    
##                                         NULL:Oceania   
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:Asia      
##                                         NULL:Americas  
##                                         NULL:Africa    
##                                         NULL:Europe    
##                                         NULL:Europe    
##                                         NULL:Asia      
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:Oceania   
##                                         NULL:Europe    
##                                         NULL:Americas  
##                                         NULL:Asia      
##                                         NULL:Americas  
##                                         NULL:Americas  
##                                         NULL:Oceania   
##                                         NULL:Africa    
##                                         NULL:Asia      
##                                         NULL:Africa    
##                                         NULL:Africa    
##  subregion                       
##  NULL:Southern Asia              
##  NULL:Southern Europe            
##  NULL:Northern Africa            
##  NULL:Polynesia                  
##  NULL:Southern Europe            
##  NULL:Middle Africa              
##  NULL:Caribbean                  
##  NULL:NA                         
##  NULL:Caribbean                  
##  NULL:South America              
##  NULL:Western Asia               
##  NULL:Caribbean                  
##  NULL:NA                         
##  NULL:Australia and New Zealand  
##  NULL:Western Europe             
##  NULL:Western Asia               
##  NULL:NA                         
##  NULL:Caribbean                  
##  NULL:Western Asia               
##  NULL:Southern Asia              
##  NULL:Caribbean                  
##  NULL:NA                         
##  NULL:Eastern Europe             
##  NULL:Western Europe             
##  NULL:Central America            
##  NULL:Western Africa             
##  NULL:Northern America           
##  NULL:Southern Asia              
##  NULL:South America              
##  NULL:NA                         
##  NULL:Southern Europe            
##  NULL:Southern Africa            
##  NULL:South America              
##  NULL:South-Eastern Asia         
##  NULL:Eastern Europe             
##  NULL:Western Africa             
##  NULL:Eastern Africa             
##  NULL:South-Eastern Asia         
##  NULL:Middle Africa              
##  NULL:Northern America           
##  NULL:NA                         
##  NULL:Western Africa             
##  NULL:Caribbean                  
##  NULL:Middle Africa              
##  NULL:Middle Africa              
##  NULL:NA                         
##  NULL:South America              
##  NULL:Eastern Asia               
##  NULL:NA                         
##  NULL:NA                         
##  NULL:South America              
##  NULL:Eastern Africa             
##  NULL:Polynesia                  
##  NULL:Central America            
##  NULL:Southern Europe            
##  NULL:Caribbean                  
##  NULL:Caribbean                  
##  NULL:Southern Europe            
##  NULL:Eastern Europe             
##  NULL:Middle Africa              
##  NULL:Northern Europe            
##  NULL:Eastern Africa             
##  NULL:Caribbean                  
##  NULL:Caribbean                  
##  NULL:South America              
##  NULL:Northern Africa            
##  NULL:Central America            
##  NULL:Middle Africa              
##  NULL:Eastern Africa             
##  NULL:Northern Europe            
##  NULL:Eastern Africa             
##  NULL:South America              
##  NULL:Northern Europe            
##  NULL:Melanesia                  
##  NULL:Northern Europe            
##  NULL:Western Europe             
##  NULL:South America              
##  NULL:Polynesia                  
##  NULL:NA                         
##  NULL:Middle Africa              
##  NULL:Western Africa             
##  NULL:Western Asia               
##  NULL:Western Europe             
##  NULL:Western Africa             
##  NULL:Southern Europe            
##  NULL:Northern America           
##  NULL:Caribbean                  
##  NULL:NA                         
##  NULL:Caribbean                  
##  NULL:Micronesia                 
##  NULL:Central America            
##  NULL:Northern Europe            
##  NULL:Western Africa             
##  NULL:Western Africa             
##  NULL:South America              
##  NULL:Caribbean                  
##  NULL:NA                         
##  NULL:Central America            
##  NULL:Eastern Europe             
##  NULL:Northern Europe            
##  NULL:Southern Asia              
##  NULL:South-Eastern Asia         
##  NULL:Southern Asia              
##  NULL:Western Asia               
##  NULL:Northern Europe            
##  NULL:Northern Europe            
##  NULL:Western Asia               
##  NULL:Southern Europe            
##  NULL:Western Africa             
##  NULL:Caribbean                  
##  NULL:Eastern Asia               
##  NULL:NA                         
##  NULL:Western Asia               
##  NULL:Central Asia               
##  NULL:Eastern Africa             
##  NULL:Micronesia                 
##  NULL:NA                         
##  NULL:Western Asia               
##  NULL:Central Asia               
##  NULL:South-Eastern Asia         
##  NULL:Northern Europe            
##  NULL:Western Asia               
##  NULL:Southern Africa            
##  NULL:Western Africa             
##  NULL:Northern Africa            
##  NULL:Western Europe             
##  NULL:Northern Europe            
##  NULL:Western Europe             
##  NULL:Southern Europe            
##  NULL:Eastern Africa             
##  NULL:NA                         
##  NULL:Eastern Africa             
##  NULL:South-Eastern Asia         
##  NULL:Southern Asia              
##  NULL:Western Africa             
##  NULL:Southern Europe            
##  NULL:Micronesia                 
##  NULL:Caribbean                  
##  NULL:Western Africa             
##  NULL:Eastern Africa             
##  NULL:Eastern Africa             
##  NULL:Central America            
##  NULL:NA                         
##  NULL:Eastern Europe             
##  NULL:Western Europe             
##  NULL:Eastern Asia               
##  NULL:Southern Europe            
##  NULL:Caribbean                  
##  NULL:Northern Africa            
##  NULL:Eastern Africa             
##  NULL:South-Eastern Asia         
##  NULL:Southern Africa            
##  NULL:Micronesia                 
##  NULL:Southern Asia              
##  NULL:Western Europe             
##  NULL:NA                         
##  NULL:Melanesia                  
##  NULL:Australia and New Zealand  
##  NULL:Central America            
##  NULL:Western Africa             
##  NULL:Western Africa             
##  NULL:Polynesia                  
##  NULL:NA                         
##  NULL:Eastern Asia               
##  NULL:Micronesia                 
##  NULL:Northern Europe            
##  NULL:Western Asia               
##  NULL:Southern Asia              
##  NULL:Micronesia                 
##  NULL:Western Asia               
##  NULL:Central America            
##  NULL:Melanesia                  
##  NULL:South America              
##  NULL:South America              
##  NULL:South-Eastern Asia         
##  NULL:NA                         
##  NULL:Eastern Europe             
##  NULL:Southern Europe            
##  NULL:Caribbean                  
##  NULL:Western Asia               
##  NULL:Middle Africa              
##  NULL:Eastern Africa             
##  NULL:Eastern Europe             
##  NULL:Eastern Europe             
##  NULL:Eastern Africa             
##  NULL:NA                         
##  NULL:NA                         
##  NULL:Western Africa             
##  NULL:Caribbean                  
##  NULL:Caribbean                  
##  NULL:NA                         
##  NULL:Northern America           
##  NULL:Caribbean                  
##  NULL:Polynesia                  
##  NULL:Southern Europe            
##  NULL:Middle Africa              
##  NULL:Western Asia               
##  NULL:Western Africa             
##  NULL:Southern Europe            
##  NULL:Eastern Africa             
##  NULL:NA                         
##  NULL:Western Africa             
##  NULL:South-Eastern Asia         
##  NULL:NA                         
##  NULL:Caribbean                  
##  NULL:Eastern Europe             
##  NULL:Southern Europe            
##  NULL:Melanesia                  
##  NULL:Eastern Africa             
##  NULL:Southern Africa            
##  NULL:NA                         
##  NULL:Eastern Asia               
##  NULL:NA                         
##  NULL:Eastern Africa             
##  NULL:Southern Europe            
##  NULL:Southern Asia              
##  NULL:Northern Africa            
##  NULL:South America              
##  NULL:Southern Africa            
##  NULL:Northern Europe            
##  NULL:Western Europe             
##  NULL:Western Asia               
##  NULL:Eastern Asia               
##  NULL:Central Asia               
##  NULL:Eastern Africa             
##  NULL:South-Eastern Asia         
##  NULL:South-Eastern Asia         
##  NULL:NA                         
##  NULL:Western Africa             
##  NULL:Polynesia                  
##  NULL:Caribbean                  
##  NULL:Northern Africa            
##  NULL:Western Asia               
##  NULL:Central Asia               
##  NULL:Caribbean                  
##  NULL:Eastern Africa             
##  NULL:Northern Europe            
##  NULL:Eastern Europe             
##  NULL:Western Asia               
##  NULL:South America              
##  NULL:Northern America           
##  NULL:Central Asia               
##  NULL:Melanesia                  
##  NULL:Southern Europe            
##  NULL:South America              
##  NULL:South-Eastern Asia         
##  NULL:Caribbean                  
##  NULL:Caribbean                  
##  NULL:Polynesia                  
##  NULL:Northern Africa            
##  NULL:Western Asia               
##  NULL:Eastern Africa             
##  NULL:Eastern Africa

# Call str() with max.level = 2 on countries_spdf
str(countries_spdf, max.level=2)

## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
##   ..@ data       :'data.frame':  253 obs. of  7 variables:
##   ..@ polygons   :List of 253
##   .. .. [list output truncated]
##   ..@ plotOrder  : int [1:253] 8 184 40 241 48 33 14 86 114 10 ...
##   ..@ bbox       : num [1:2, 1:2] -180 -85.2 190.3 83.6
##   .. ..- attr(*, "dimnames")=List of 2
##   ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot

# Plot countries_spdf
plot(countries_spdf)

# 169th element of countries_spdf@polygons: one
one <- countries_sp@polygons[[169]]

# Print one
print(one)

## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] 131.161721   3.040651
## 
## Slot "area":
## [1] 0.001400832
## 
## Slot "hole":
## [1] FALSE
## 
## Slot "ringDir":
## [1] 1
## 
## Slot "coords":
##           [,1]     [,2]
##  [1,] 131.1724 3.026221
##  [2,] 131.1496 3.021875
##  [3,] 131.1350 3.025244
##  [4,] 131.1367 3.039453
##  [5,] 131.1516 3.054101
##  [6,] 131.1724 3.060596
##  [7,] 131.1879 3.055615
##  [8,] 131.1863 3.042090
##  [9,] 131.1724 3.026221
## 
## 
## [[2]]
## An object of class "Polygon"
## Slot "labpt":
## [1] 134.580483   7.512956
## 
## Slot "area":
## [1] 0.0263786
## 
## Slot "hole":
## [1] FALSE
## 
## Slot "ringDir":
## [1] 1
## 
## Slot "coords":
##           [,1]     [,2]
##  [1,] 134.5954 7.382031
##  [2,] 134.5347 7.360645
##  [3,] 134.5062 7.437109
##  [4,] 134.5157 7.525781
##  [5,] 134.5560 7.593945
##  [6,] 134.5997 7.615772
##  [7,] 134.6087 7.623584
##  [8,] 134.6512 7.712109
##  [9,] 134.6596 7.663281
## [10,] 134.6327 7.501318
## [11,] 134.5983 7.438281
## [12,] 134.5954 7.382031
## 
## 
## 
## Slot "plotOrder":
## [1] 2 1
## 
## Slot "labpt":
## [1] 134.580483   7.512956
## 
## Slot "ID":
## [1] "Palau"
## 
## Slot "area":
## [1] 0.02777943

# Call summary() on one
summary(one)

##   Length    Class     Mode 
##        1 Polygons       S4

# Call str() on one with max.level = 2
str(one, max.level=2)

## Formal class 'Polygons' [package "sp"] with 5 slots
##   ..@ Polygons :List of 2
##   ..@ plotOrder: int [1:2] 2 1
##   ..@ labpt    : num [1:2] 134.58 7.51
##   ..@ ID       : chr "Palau"
##   ..@ area     : num 0.0278

# Grab the USA (happens to be 241) and name it as one
one <- countries_sp@polygons[[241]]

# str() with max.level = 2, on the Polygons slot of one
str(one@Polygons, max.level=2)

## List of 127
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##  $ :Formal class 'Polygon' [package "sp"] with 5 slots
##   [list output truncated]

# str() with max.level = 2, on the 6th element of the one@Polygons (audibles to #59, which is the continental USA)
str(one@Polygons[[59]], max.level=2)

## Formal class 'Polygon' [package "sp"] with 5 slots
##   ..@ labpt  : num [1:2] -99.1 39.5
##   ..@ area   : num 834
##   ..@ hole   : logi FALSE
##   ..@ ringDir: int 1
##   ..@ coords : num [1:1990, 1:2] -74.7 -74.7 -74.4 -74 -73.6 ...

# Call plot on the coords slot of 6th element of one@Polygons (audibles to #59, which is the continental USA)
plot(one@Polygons[[59]]@coords)

# Subset the 169th object of countries_spdf: usa (audibled this to use countries_sp and number 241)
usa <- countries_sp[241, ]

# Look at summary() of usa
summary(usa)

## Object of class SpatialPolygons
## Coordinates:
##          min       max
## x -178.19452 179.77998
## y   18.96392  71.40767
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0]

# Look at str() of usa
str(usa, max.level=2)

## Formal class 'SpatialPolygons' [package "sp"] with 4 slots
##   ..@ polygons   :List of 1
##   ..@ plotOrder  : int 1
##   ..@ bbox       : num [1:2, 1:2] -178.2 19 179.8 71.4
##   .. ..- attr(*, "dimnames")=List of 2
##   ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot

# Call plot() on usa
plot(usa)

## *** MAY NEED TO SKIP UNLESS CAN FIND/CREATE THE spdf ["name", "iso_a3", "population", "gdp", "region", "subregion"]
# Call head() and str() on the data slot of countries_spdf
head(countries_spdf@data)

##                ctr           name iso_a3 population    gdp  region
## Afghanistan      1    Afghanistan    AFG   33369945  18395    Asia
## Albania          2        Albania    ALB    2903700  12144  Europe
## Algeria          3        Algeria    DZA   40375954 168318  Africa
## American Samoa   4 American Samoa    ASM      55602     NA Oceania
## Andorra          5        Andorra    AND      69165     NA  Europe
## Angola           6         Angola    AGO   25830958  91939  Africa
##                      subregion
## Afghanistan      Southern Asia
## Albania        Southern Europe
## Algeria        Northern Africa
## American Samoa       Polynesia
## Andorra        Southern Europe
## Angola           Middle Africa

str(countries_spdf@data)

## 'data.frame':    253 obs. of  7 variables:
##  $ ctr       : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ name      : chr  "Afghanistan" "Albania" "Algeria" "American Samoa" ...
##  $ iso_a3    : chr  "AFG" "ALB" "DZA" "ASM" ...
##  $ population: num  33369945 2903700 40375954 55602 69165 ...
##  $ gdp       : num  18395 12144 168318 NA NA ...
##  $ region    : chr  "Asia" "Europe" "Africa" "Oceania" ...
##  $ subregion : chr  "Southern Asia" "Southern Europe" "Northern Africa" "Polynesia" ...

# Pull out the name column using $
str(countries_spdf$name)

##  chr [1:253] "Afghanistan" "Albania" "Algeria" ...

# Pull out the subregion column using [[
str(countries_spdf[["subregion"]])

##  chr [1:253] "Southern Asia" "Southern Europe" "Northern Africa" ...

# Create logical vector: is_nz
is_nz <- countries_spdf$name == "New Zealand"

# Subset countries_spdf using is_nz: nz
nz <- countries_spdf[is_nz, ]

# Plot nz
plot(nz)

library(tmap)

# Use qtm() to create a choropleth map of gdp
qtm(shp = countries_spdf, fill = "gdp")

# Add style argument to the tm_fill() call
tm_shape(countries_spdf) +
  tm_fill(col = "population", style="quantile") +
  # Add a tm_borders() layer 
  tm_borders(col = "burlywood4")

# New plot, with tm_bubbles() instead of tm_fill()
tm_shape(countries_spdf) +
  tm_bubbles(size = "population") +
  # Add a tm_borders() layer 
  tm_borders(col = "burlywood4")

# Switch to a Hobo-Dyer projection (there is a problem with 74 (Fiji) and 184 (Russia))
tm_shape(countries_spdf[-c(74, 184), ], projection="hd") +
  tm_grid(n.x = 11, n.y = 11) +
  tm_fill(col = "population", style = "quantile")  +
  tm_borders(col = "burlywood4")

# Switch to a Robinson projection
tm_shape(countries_spdf[-c(74, 184), ], projection="robin") +
  tm_grid(n.x = 11, n.y = 11) +
  tm_fill(col = "population", style = "quantile")  +
  tm_borders(col = "burlywood4")

# Add tm_style_classic() to your plot
tm_shape(countries_spdf[-c(74, 184), ], projection="robin") +
  tm_grid(n.x = 11, n.y = 11) +
  tm_fill(col = "population", style = "quantile")  +
  tm_borders(col = "burlywood4") +
  tm_style_classic()

# Plot from last exercise
tm_shape(countries_spdf[-c(74, 184), ]) +
  tm_grid(n.x = 11, n.y = 11, projection = "longlat") +
  tm_fill(col = "population", style = "quantile")  +
  tm_borders(col = "burlywood4")

# Save a static version "population.png"
save_tmap(filename = "population.png")

## Map saved to C:\Users\Dave\Documents\Personal\Learning\Coursera\RDirectory\RHomework\DataCamp\population.png

## Resolution: 2100 by 1500 pixels

## Size: 6.999999 by 4.999999 inches (300 dpi)

# Save an interactive version "population.html" (not run due to ActiveX blocking)
# save_tmap(filename = "population.html")

# Attempt to fix the Russia problem (only partially works)
is0 <- countries_spdf
slot(is0, "polygons") <- lapply(slot(is0, "polygons"), maptools::checkPolygonsHoles)

## Warning in checkPolygonsGEOS(x, properly = properly, useSTRtree =
## useSTRtree): Duplicate Polygon objects dropped: 2

## Warning in checkPolygonsGEOS(x, properly = properly, useSTRtree =
## useSTRtree): Duplicate Polygon objects dropped: 2

tm_shape(countries_spdf[-c(74, 184), ], projection="hd") +
  tm_grid(n.x = 11, n.y = 11) +
  tm_fill(col = "population", style = "quantile")  +
  tm_borders(col = "burlywood4")

tm_shape(is0, projection="hd") +
  tm_grid(n.x = 11, n.y = 11) +
  tm_fill(col = "population", style = "quantile")  +
  tm_borders(col = "burlywood4")

tm_shape(countries_spdf[-c(74, 184), ], projection="robin") +
  tm_grid(n.x = 11, n.y = 11) +
  tm_fill(col = "population", style = "quantile")  +
  tm_borders(col = "burlywood4") +
  tm_style_classic()

tm_shape(is0, projection="robin") +
  tm_grid(n.x = 11, n.y = 11) +
  tm_fill(col = "population", style = "quantile")  +
  tm_borders(col = "burlywood4") +
  tm_style_classic()

Chapter 3 - Raster Data and Color

Data frames are not an ideal way to store spatial data - duplication of data per polygon/point, no CRS storage, inefficient graphing/display:

• Matrices tend to be a better structure – each cell is a data point, with information/CRS layered on top
  
• Multi-layer (multi-band) rasters would have multiple matrices each representing multiple types of data
  
• There are some raster data classes in library(sp) - SpatialGrid, SpatialPixels, SpatialGridDataFrame, SpatialPixelsDataFrame
  
• However, library(raster) is preferred for working with rasters - easier import, better memory usage, faster functions/operations, still uses S4 (and many of the same functions)
  
• The raster::print() of a file like countries_spdf is much more compact and concise than the equivalent sp::print()

Humans perceive color as trichromtic and three-dimensional (HCL) - hue, chroma, and luminance:

• Hue is the predominant color wavelength; generally considered to be circular and unordered (no real sense of highest and lowest)
  
• Chroma is the distinction from gray (muddy) to vibrant – order is generally agreed
  
• Luminance defines the lightness of a color, from black at one end to white at the other – order is generally agreed
  
• If the goal is to show a higher value, using the ordered dimensions (Chroma, Luminance) is helpful

Color palettes help to convey these key meanings, and are implemented in various R packages:

• Sequential (ordered) palettes generally have steps in chroma and/or luminance, possibly with varying hue as a redundancy for finding values on the scale
  
• Diverging (ordered in two directions - like distance from centroid) will generally have steps in chroma and/or luminance, with hue distinguishing the direction
  
• Qualitative (unordered) will have steps in hue, but all with the same chroma/luminance to define that there are no higher/lower values
  
• The library(RColorBrewer) is especially useful for this – see RColorBrewer::display.brewer.all() or RColorBrewer::brewer.pal(n=, )
  
• The library(viridisLite) is also helpful, with viridis(n=) being analogous in many ways to brewer.pal()

Mapping of numbers to colors can vary by package:

• ggplot2 will map to a continuous gradient of colors
  
• tmap will map to a discrete bucket of colors
  
• Continuous colors have the benefit of being conceptually uniform; two objects the same distance apart will appear to be the same amount of color different
  
• Discrete scales allow for greater control over colors, and are generally easier for users to lookup
  
• The library(classInt) allows for classInt::classIntervals(values, n=, style=), which can be useful for binning
  • style “equal” will make each bin interval the same, while “quantile” puts roughly the same n in each bin, while “pretty” attempts to make nice cut points, and style “fixed” lets you choose your own

Example code includes:

library(raster)

## Warning: package 'raster' was built under R version 3.2.5

## 
## Attaching package: 'raster'

## The following object is masked from 'package:dplyr':
## 
##     select

## The following objects are masked from 'package:Hmisc':
## 
##     mask, zoom

## The following objects are masked from 'package:MASS':
## 
##     area, select

## The following object is masked from 'package:tidyr':
## 
##     extract

### *** LIKELY NEED TO COMMENT OUT DUE TO NOT HAVING FILE "pop"
# Print pop
# print(pop)

# Call str() on pop, with max.level = 2
# str(pop, max.level=2)

# Call summary on pop
# summary(pop)


# Call plot() on pop
# plot(pop)

# Call str() on values(pop)
# str(values(pop))

# Call head() on values(pop)
# head(values(pop))


# Print pop_by_age
# print(pop_by_age)

# Subset out the under_1 layer using [[
# pop_by_age[["under_1"]]

# Plot the under_1 layer
# plot(pop_by_age[["under_1"]])


library(tmap)

# Specify pop as the shp and add a tm_raster() layer
# tm_shape(pop) +
#   tm_raster()

# Plot the under_1 layer in pop_by_age
# tm_shape(pop_by_age) +
#   tm_raster(col="under_1")


# Call levelplot() on pop
# rasterVis::levelplot(pop)


hs_preds <- 
"259 ; 257 ; 256 ; 254 ; 252 ; 250 ; 248 ; 246 ; 245 ; 243 ; 241 ; 239 ; 238 ; 237 ; 236 ; 235 ; 235 ; 236 ; 237 ; 239 ; 241 ; 243 ; 245 ; 248 ; 251 ; 254 ; 257 ; 259 ; 262 ; 265 ; 267 ; 270 ; 272 ; 274 ; 275 ; 277 ; 278 ; 279 ; 280 ; 281 ; 257 ; 255 ; 254 ; 252 ; 250 ; 249 ; 247 ; 245 ; 244 ; 242 ; 240 ; 239 ; 237 ; 236 ; 236 ; 235 ; 235 ; 236 ; 237 ; 238 ; 240 ; 243 ; 245 ; 248 ; 250 ; 253 ; 256 ; 258 ; 261 ; 263 ; 266 ; 268 ; 270 ; 272 ; 274 ; 275 ; 277 ; 278 ; 278 ; 279 ; 254 ; 253 ; 252 ; 250 ; 249 ; 247 ; 246 ; 245 ; 243 ; 242 ; 240 ; 239 ; 237 ; 236 ; 236 ; 235 ; 236 ; 236 ; 237 ; 239 ; 240 ; 243 ; 245 ; 247 ; 250 ; 252 ; 254 ; 257 ; 259 ; 262 ; 264 ; 266 ; 268 ; 270 ; 272 ; 273 ; 274 ; 275 ; 276 ; 277 ; 252 ; 250 ; 249 ; 248 ; 247 ; 246 ; 245 ; 244 ; 243 ; 241 ; 240 ; 239 ; 238 ; 237 ; 236 ; 236 ; 236 ; 237 ; 238 ; 239 ; 241 ; 243 ; 245 ; 247 ; 249 ; 251 ; 253 ; 255 ; 258 ; 260 ; 262 ; 264 ; 266 ; 268 ; 269 ; 271 ; 272 ; 273 ; 274 ; 274 ; 249 ; 248 ; 247 ; 246 ; 245 ; 245 ; 244 ; 243 ; 242 ; 241 ; 240 ; 239 ; 238 ; 237 ; 237 ; 237 ; 237 ; 237 ; 238 ; 240 ; 241 ; 243 ; 244 ; 246 ; 248 ; 250 ; 252 ; 254 ; 256 ; 257 ; 259 ; 261 ; 263 ; 264 ; 266 ; 267 ; 268 ; 269 ; 270 ; 271 ; 246 ; 245 ; 245 ; 244 ; 243 ; 243 ; 242 ; 242 ; 241 ; 240 ; 240 ; 239 ; 238 ; 237 ; 237 ; 237 ; 237 ; 238 ; 239 ; 240 ; 241 ; 243 ; 244 ; 246 ; 247 ; 249 ; 250 ; 252 ; 253 ; 254 ; 256 ; 257 ; 259 ; 260 ; 262 ; 263 ; 264 ; 265 ; 266 ; 267 ; 243 ; 242 ; 242 ; 241 ; 241 ; 240 ; 240 ; 240 ; 239 ; 239 ; 238 ; 238 ; 237 ; 237 ; 237 ; 237 ; 237 ; 238 ; 239 ; 240 ; 241 ; 242 ; 244 ; 245 ; 246 ; 247 ; 248 ; 249 ; 250 ; 251 ; 252 ; 253 ; 254 ; 255 ; 256 ; 258 ; 259 ; 260 ; 261 ; 263 ; 239 ; 239 ; 238 ; 238 ; 238 ; 238 ; 237 ; 237 ; 237 ; 237 ; 237 ; 236 ; 236 ; 236 ; 236 ; 237 ; 237 ; 238 ; 239 ; 240 ; 241 ; 242 ; 243 ; 244 ; 245 ; 245 ; 246 ; 246 ; 246 ; 247 ; 247 ; 248 ; 249 ; 250 ; 251 ; 252 ; 253 ; 255 ; 256 ; 258 ; 236 ; 235 ; 235 ; 235 ; 234 ; 234 ; 234 ; 234 ; 234 ; 234 ; 234 ; 234 ; 234 ; 235 ; 235 ; 236 ; 236 ; 237 ; 238 ; 195 ; 195 ; 195 ; 195 ; 196 ; 196 ; 196 ; 196 ; 197 ; 197 ; 198 ; 198 ; 199 ; 200 ; 201 ; 202 ; 203 ; 205 ; 208 ; 204 ; 198 "

preds <- data.frame(lat=rep(1:9, each=40), 
                    lon=rep(1:40, times=9), 
                    predicted_price=as.numeric(strsplit(hs_preds, " ; ")[[1]])
                    )

library(RColorBrewer)
# 9 steps on the RColorBrewer "BuPu" palette: blups
blups <- brewer.pal(n=9, "BuPu")

# Add scale_fill_gradientn() with the blups palette
ggplot(preds) +
  geom_tile(aes(lon, lat, fill = predicted_price), alpha = 0.8) +
  scale_fill_gradientn(colors=blups)

library(viridisLite)

## Warning: package 'viridisLite' was built under R version 3.2.5

## 
## Attaching package: 'viridisLite'

## The following objects are masked from 'package:viridis':
## 
##     inferno, magma, plasma, viridis

# viridisLite viridis palette with 9 steps: vir
vir <- viridis(n=9)

# Add scale_fill_gradientn() with the vir palette
ggplot(preds) +
  geom_tile(aes(lon, lat, fill = predicted_price), alpha = 0.8) +
  scale_fill_gradientn(colors=vir)

# mag: a viridisLite magma palette with 9 steps
mag <- magma(n=9)

# Add scale_fill_gradientn() with the mag palette
ggplot(preds) +
  geom_tile(aes(lon, lat, fill = predicted_price), alpha = 0.8) +
  scale_fill_gradientn(colors=mag)

# Generate palettes from last time
library(RColorBrewer)
blups <- brewer.pal(9, "BuPu")

library(viridisLite)
vir <- viridis(9)
mag <- magma(9)

# Use the blups palette
# tm_shape(prop_by_age) +
#   tm_raster("age_18_24", palette=blups) +
#   tm_legend(position = c("right", "bottom"))

# Use the vir palette
# tm_shape(prop_by_age) +
#   tm_raster("age_18_24", palette=vir) +
#   tm_legend(position = c("right", "bottom"))

# Use the mag palette but reverse the order
# tm_shape(prop_by_age) +
#   tm_raster("age_18_24", palette=rev(mag)) +
#   tm_legend(position = c("right", "bottom"))


mag <- viridisLite::magma(7)
library(classInt)

## Warning: package 'classInt' was built under R version 3.2.5

# Create 5 "pretty" breaks with classIntervals()
# classIntervals(values(prop_by_age[["age_18_24"]]), n=5, style="pretty")

# Create 5 "quantile" breaks with classIntervals()
# classIntervals(values(prop_by_age[["age_18_24"]]), n=5, style="quantile")

# Use 5 "quantile" breaks in tm_raster()
# tm_shape(prop_by_age) +
#   tm_raster("age_18_24", palette = mag, n=5, style="quantile") +
#   tm_legend(position = c("right", "bottom"))

# Create histogram of proportions
# hist(values(prop_by_age[["age_18_24"]]))

# Use fixed breaks in tm_raster()
# tm_shape(prop_by_age) +
#   tm_raster("age_18_24", palette = mag,
#     style = "fixed", breaks=c(0.025, 0.05, 0.1, 0.2, 0.25, 0.3, 1))

# Save your plot to "prop_18-24.html"
# save_tmap(filename="prop_18-24.html")


# Print migration
# print(migration)

# Diverging "RdGy" palette
red_gray <- brewer.pal(n=7, "RdGy")

# Use red_gray as the palette 
# tm_shape(migration) +
#   tm_raster(palette=red_gray) +
#   tm_legend(outside = TRUE, outside.position = c("bottom"))

# Add fixed breaks 
# tm_shape(migration) +
#   tm_raster(palette=red_gray, style="fixed", breaks=c(-5e6, -5e3, -5e2, -5e1, 5e1, 5e2, 5e3, 5e6)) +
#   tm_legend(outside = TRUE, outside.position = c("bottom"))


library(raster)

# Plot land_cover
# tm_shape(land_cover) + 
#   tm_raster()

# Palette like the ggplot2 default
hcl_cols <- hcl(h = seq(15, 375, length = 9), 
                c = 100, l = 65)[-9]

# Use hcl_cols as the palette
# tm_shape(land_cover) + 
#   tm_raster(palette=hcl_cols)


# Examine levels of land_cover
# levels(land_cover)

# A set of intuitive colors
intuitive_cols <- c(
  "darkgreen",
  "darkolivegreen4",
  "goldenrod2",
  "seagreen",
  "wheat",
  "slategrey",
  "white",
  "lightskyblue1"
)

# Use intuitive_cols as palette
# tm_shape(land_cover) + 
#   tm_raster(palette=intuitive_cols) +
#   tm_legend(position = c("left", "bottom"))

Chapter 4 - Data Import and Projections

Reading in spatial data - case study for creating map of income by census tract in Manhattan:

• Spatial objects can include 1) census tract polygons, 2) larger neighborhood polygons, and 3) areas of water polygons
  
• Procedures typically include 1) reading in shape files, 2) aligning all CRS (coordinate reference systems, and 3) merging in data to form a SpatialPolygonsDataFrame
  
• Shape files (points, lines, polygons) can be read in using rgdal::readOGR()
  • There are often many files in a single directory, each with same name but different file extension
    
  • water <- rgdal::readOGR(“water”, “water-areas”) # reads in water-areas. from ./water directory
    
• Raster files can be read in using rgdal::readGDAL() which will convert them to sp data, or using raster::raster() which will leave them as raster objects

Coordinate reference systems stored with the data can be accessed using proj4string():

• There are actually more than one lat/long, each defined by 1) an ellipsoid representing the non-spherical earth, and 2) a datum representing a position of the ellipsoid relative to the earth
  • WGS84 datum with WGS84 ellipsoid is commonly used for global data
    
  • NAD83 datum with GRS80 ellipsoid is commonly used for US data
    
• A WGS84 CRS can be assigned to file x currently lacking any CRS using proj4string(x) <- “+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0”
  
• If x already has a CRS and you want a different CRS, then you need to transform the CRS (cannot just reassign using proj4string(x) <- “my desired new CRS”
  • sp::spTransform(x, CRSobj, . . . ) will run the conversion
    
  • Common use might be sp::spTrasnform(x, proj4string(y)) to convert the x data to the y projection, allowing these data to be used together

Adding data to spatial objects:

• Income data is available from acs, and can be found in the acs package – desire is for nyc_income, a 288x6 frame with name, state, county, tract, estimate, se
  
• Can get the ID slots in a given SpatialPolygons object with sapply(@polygons, function(x) { x@ID } )
  
• When running SpatialPolygonsDataFrame(Sr, data, match.ID = TRUE), then the rownames of the data are matched up to the ID slots for the Sr
  • If instead match.ID = FALSE, then the items are matched up 1:1 assuming they both have the same IDs and do not need any re-ordering
    
• Generally, the safest way to work with the data is to keep match.id=TRUE and to use rownames for the data and force them to match to the various ID slots
  • Equally, the sp::merge() is like base merge but for polygon objects; it also helps to ensure that data are aligned to polygons as intended

Polishing the map can make it even better - iterative process to improve the visuals:

1. Remove distractions, and let the data shine
   
2. Make sure that any spatial context is useful (e.g., roads may help orient the reader, but the roads should not be the central focal point)
   
3. Double-check that the titles, legends, and labels are clear even to a non-SME
   
4. Annotate the plot; point out anything important, and attribute data sources

Example code includes:

## downloaded "nynta_16d" from https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-nynta.page)

library(sp)
library(rgdal)

# Call dir() with directory name
dir("./nynta_16d")

## [1] "nynta.dbf"     "nynta.prj"     "nynta.shp"     "nynta.shp.xml"
## [5] "nynta.shx"

# Read in shapefile with readOGR(): neighborhoods
neighborhoods <- readOGR("nynta_16d", "nynta")

## OGR data source with driver: ESRI Shapefile 
## Source: "nynta_16d", layer: "nynta"
## with 195 features
## It has 7 fields

# summary() of neighborhoods
summary(neighborhoods)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##        min       max
## x 913175.1 1067382.5
## y 120121.9  272844.3
## Is projected: TRUE 
## proj4string :
## [+proj=lcc +lat_1=40.66666666666666 +lat_2=41.03333333333333
## +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000 +y_0=0
## +datum=NAD83 +units=us-ft +no_defs +ellps=GRS80 +towgs84=0,0,0]
## Data attributes:
##     BoroCode          BoroName  CountyFIPS    NTACode   
##  Min.   :1   Bronx        :38   005:38     BK09   :  1  
##  1st Qu.:2   Brooklyn     :51   047:51     BK17   :  1  
##  Median :3   Manhattan    :29   061:29     BK19   :  1  
##  Mean   :3   Queens       :58   081:58     BK21   :  1  
##  3rd Qu.:4   Staten Island:19   085:19     BK23   :  1  
##  Max.   :5                                 BK25   :  1  
##                                            (Other):189  
##                                        NTAName      Shape_Leng    
##  Airport                                   :  1   Min.   : 11000  
##  Allerton-Pelham Gardens                   :  1   1st Qu.: 23824  
##  Annadale-Huguenot-Prince's Bay-Eltingville:  1   Median : 30550  
##  Arden Heights                             :  1   Mean   : 42004  
##  Astoria                                   :  1   3rd Qu.: 41877  
##  Auburndale                                :  1   Max.   :490427  
##  (Other)                                   :189                   
##    Shape_Area       
##  Min.   :  5573902  
##  1st Qu.: 19392084  
##  Median : 32629789  
##  Mean   : 43227702  
##  3rd Qu.: 50237458  
##  Max.   :327760120  
## 

# Plot neighboorhoods
plot(neighborhoods)

## need to find/create the census tract data file in raster format
library(raster) 


# Call dir() on the directory
# dir("./nyc_grid_data")

# Use raster() with file path: income_grid
# income_grid <- raster("./nyc_grid_data/m5602ahhi00.tif")

# Call summary() on income_grid
# summary(income_grid)

# Call plot() on income_grid
# plot(income_grid)


library(sp)

# Call tracts(): nyc_tracts
nyc_tracts <- tigris::tracts(state="NY", county="New York", cb=TRUE)

# Call summary() on nyc_tracts
summary(nyc_tracts)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##         min       max
## x -74.04731 -73.90700
## y  40.68419  40.88207
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0]
## Data attributes:
##  STATEFP   COUNTYFP   TRACTCE       AFFGEOID                   
##  NULL:36   NULL:061   NULL:001401   NULL:1400000US36061001401  
##  NULL:36   NULL:061   NULL:002201   NULL:1400000US36061002201  
##  NULL:36   NULL:061   NULL:003200   NULL:1400000US36061003200  
##  NULL:36   NULL:061   NULL:004000   NULL:1400000US36061004000  
##  NULL:36   NULL:061   NULL:005501   NULL:1400000US36061005501  
##  NULL:36   NULL:061   NULL:005600   NULL:1400000US36061005600  
##  NULL:36   NULL:061   NULL:007400   NULL:1400000US36061007400  
##  NULL:36   NULL:061   NULL:007700   NULL:1400000US36061007700  
##  NULL:36   NULL:061   NULL:008200   NULL:1400000US36061008200  
##  NULL:36   NULL:061   NULL:010200   NULL:1400000US36061010200  
##  NULL:36   NULL:061   NULL:011500   NULL:1400000US36061011500  
##  NULL:36   NULL:061   NULL:012800   NULL:1400000US36061012800  
##  NULL:36   NULL:061   NULL:014401   NULL:1400000US36061014401  
##  NULL:36   NULL:061   NULL:015002   NULL:1400000US36061015002  
##  NULL:36   NULL:061   NULL:015802   NULL:1400000US36061015802  
##  NULL:36   NULL:061   NULL:017300   NULL:1400000US36061017300  
##  NULL:36   NULL:061   NULL:018500   NULL:1400000US36061018500  
##  NULL:36   NULL:061   NULL:018600   NULL:1400000US36061018600  
##  NULL:36   NULL:061   NULL:022400   NULL:1400000US36061022400  
##  NULL:36   NULL:061   NULL:023600   NULL:1400000US36061023600  
##  NULL:36   NULL:061   NULL:024900   NULL:1400000US36061024900  
##  NULL:36   NULL:061   NULL:025300   NULL:1400000US36061025300  
##  NULL:36   NULL:061   NULL:026700   NULL:1400000US36061026700  
##  NULL:36   NULL:061   NULL:031704   NULL:1400000US36061031704  
##  NULL:36   NULL:061   NULL:001300   NULL:1400000US36061001300  
##  NULL:36   NULL:061   NULL:002000   NULL:1400000US36061002000  
##  NULL:36   NULL:061   NULL:003100   NULL:1400000US36061003100  
##  NULL:36   NULL:061   NULL:003602   NULL:1400000US36061003602  
##  NULL:36   NULL:061   NULL:004300   NULL:1400000US36061004300  
##  NULL:36   NULL:061   NULL:004700   NULL:1400000US36061004700  
##  NULL:36   NULL:061   NULL:006300   NULL:1400000US36061006300  
##  NULL:36   NULL:061   NULL:008700   NULL:1400000US36061008700  
##  NULL:36   NULL:061   NULL:009500   NULL:1400000US36061009500  
##  NULL:36   NULL:061   NULL:009800   NULL:1400000US36061009800  
##  NULL:36   NULL:061   NULL:010602   NULL:1400000US36061010602  
##  NULL:36   NULL:061   NULL:011100   NULL:1400000US36061011100  
##  NULL:36   NULL:061   NULL:012700   NULL:1400000US36061012700  
##  NULL:36   NULL:061   NULL:013000   NULL:1400000US36061013000  
##  NULL:36   NULL:061   NULL:013900   NULL:1400000US36061013900  
##  NULL:36   NULL:061   NULL:014500   NULL:1400000US36061014500  
##  NULL:36   NULL:061   NULL:014801   NULL:1400000US36061014801  
##  NULL:36   NULL:061   NULL:016400   NULL:1400000US36061016400  
##  NULL:36   NULL:061   NULL:016900   NULL:1400000US36061016900  
##  NULL:36   NULL:061   NULL:017401   NULL:1400000US36061017401  
##  NULL:36   NULL:061   NULL:018100   NULL:1400000US36061018100  
##  NULL:36   NULL:061   NULL:019200   NULL:1400000US36061019200  
##  NULL:36   NULL:061   NULL:019800   NULL:1400000US36061019800  
##  NULL:36   NULL:061   NULL:020300   NULL:1400000US36061020300  
##  NULL:36   NULL:061   NULL:021200   NULL:1400000US36061021200  
##  NULL:36   NULL:061   NULL:022200   NULL:1400000US36061022200  
##  NULL:36   NULL:061   NULL:022600   NULL:1400000US36061022600  
##  NULL:36   NULL:061   NULL:023501   NULL:1400000US36061023501  
##  NULL:36   NULL:061   NULL:024100   NULL:1400000US36061024100  
##  NULL:36   NULL:061   NULL:029100   NULL:1400000US36061029100  
##  NULL:36   NULL:061   NULL:030900   NULL:1400000US36061030900  
##  NULL:36   NULL:061   NULL:000201   NULL:1400000US36061000201  
##  NULL:36   NULL:061   NULL:003002   NULL:1400000US36061003002  
##  NULL:36   NULL:061   NULL:006700   NULL:1400000US36061006700  
##  NULL:36   NULL:061   NULL:008300   NULL:1400000US36061008300  
##  NULL:36   NULL:061   NULL:011402   NULL:1400000US36061011402  
##  NULL:36   NULL:061   NULL:016500   NULL:1400000US36061016500  
##  NULL:36   NULL:061   NULL:019000   NULL:1400000US36061019000  
##  NULL:36   NULL:061   NULL:021500   NULL:1400000US36061021500  
##  NULL:36   NULL:061   NULL:023300   NULL:1400000US36061023300  
##  NULL:36   NULL:061   NULL:026500   NULL:1400000US36061026500  
##  NULL:36   NULL:061   NULL:006500   NULL:1400000US36061006500  
##  NULL:36   NULL:061   NULL:020500   NULL:1400000US36061020500  
##  NULL:36   NULL:061   NULL:024700   NULL:1400000US36061024700  
##  NULL:36   NULL:061   NULL:001001   NULL:1400000US36061001001  
##  NULL:36   NULL:061   NULL:003001   NULL:1400000US36061003001  
##  NULL:36   NULL:061   NULL:006100   NULL:1400000US36061006100  
##  NULL:36   NULL:061   NULL:010100   NULL:1400000US36061010100  
##  NULL:36   NULL:061   NULL:014000   NULL:1400000US36061014000  
##  NULL:36   NULL:061   NULL:020701   NULL:1400000US36061020701  
##  NULL:36   NULL:061   NULL:000700   NULL:1400000US36061000700  
##  NULL:36   NULL:061   NULL:000900   NULL:1400000US36061000900  
##  NULL:36   NULL:061   NULL:001502   NULL:1400000US36061001502  
##  NULL:36   NULL:061   NULL:002400   NULL:1400000US36061002400  
##  NULL:36   NULL:061   NULL:002800   NULL:1400000US36061002800  
##  NULL:36   NULL:061   NULL:003601   NULL:1400000US36061003601  
##  NULL:36   NULL:061   NULL:003700   NULL:1400000US36061003700  
##  NULL:36   NULL:061   NULL:004800   NULL:1400000US36061004800  
##  NULL:36   NULL:061   NULL:005200   NULL:1400000US36061005200  
##  NULL:36   NULL:061   NULL:005700   NULL:1400000US36061005700  
##  NULL:36   NULL:061   NULL:006400   NULL:1400000US36061006400  
##  NULL:36   NULL:061   NULL:006600   NULL:1400000US36061006600  
##  NULL:36   NULL:061   NULL:006900   NULL:1400000US36061006900  
##  NULL:36   NULL:061   NULL:007000   NULL:1400000US36061007000  
##  NULL:36   NULL:061   NULL:007100   NULL:1400000US36061007100  
##  NULL:36   NULL:061   NULL:007500   NULL:1400000US36061007500  
##  NULL:36   NULL:061   NULL:007600   NULL:1400000US36061007600  
##  NULL:36   NULL:061   NULL:008000   NULL:1400000US36061008000  
##  NULL:36   NULL:061   NULL:008100   NULL:1400000US36061008100  
##  NULL:36   NULL:061   NULL:008601   NULL:1400000US36061008601  
##  NULL:36   NULL:061   NULL:008900   NULL:1400000US36061008900  
##  NULL:36   NULL:061   NULL:009600   NULL:1400000US36061009600  
##  NULL:36   NULL:061   NULL:009900   NULL:1400000US36061009900  
##  NULL:36   NULL:061   NULL:011700   NULL:1400000US36061011700  
##  NULL:36   NULL:061   NULL:012600   NULL:1400000US36061012600  
##  NULL:36   NULL:061   NULL:013100   NULL:1400000US36061013100  
##  NULL:36   NULL:061   NULL:013600   NULL:1400000US36061013600  
##  NULL:36   NULL:061   NULL:014200   NULL:1400000US36061014200  
##  NULL:36   NULL:061   NULL:014700   NULL:1400000US36061014700  
##  NULL:36   NULL:061   NULL:014900   NULL:1400000US36061014900  
##  NULL:36   NULL:061   NULL:015700   NULL:1400000US36061015700  
##  NULL:36   NULL:061   NULL:016002   NULL:1400000US36061016002  
##  NULL:36   NULL:061   NULL:016300   NULL:1400000US36061016300  
##  NULL:36   NULL:061   NULL:017000   NULL:1400000US36061017000  
##  NULL:36   NULL:061   NULL:017402   NULL:1400000US36061017402  
##  NULL:36   NULL:061   NULL:019300   NULL:1400000US36061019300  
##  NULL:36   NULL:061   NULL:019500   NULL:1400000US36061019500  
##  NULL:36   NULL:061   NULL:019900   NULL:1400000US36061019900  
##  NULL:36   NULL:061   NULL:020800   NULL:1400000US36061020800  
##  NULL:36   NULL:061   NULL:011300   NULL:1400000US36061011300  
##  NULL:36   NULL:061   NULL:021303   NULL:1400000US36061021303  
##  NULL:36   NULL:061   NULL:022301   NULL:1400000US36061022301  
##  NULL:36   NULL:061   NULL:022700   NULL:1400000US36061022700  
##  NULL:36   NULL:061   NULL:023200   NULL:1400000US36061023200  
##  NULL:36   NULL:061   NULL:023801   NULL:1400000US36061023801  
##  NULL:36   NULL:061   NULL:024200   NULL:1400000US36061024200  
##  NULL:36   NULL:061   NULL:024301   NULL:1400000US36061024301  
##  NULL:36   NULL:061   NULL:024500   NULL:1400000US36061024500  
##  NULL:36   NULL:061   NULL:025100   NULL:1400000US36061025100  
##  NULL:36   NULL:061   NULL:025900   NULL:1400000US36061025900  
##  NULL:36   NULL:061   NULL:026100   NULL:1400000US36061026100  
##  NULL:36   NULL:061   NULL:026900   NULL:1400000US36061026900  
##  NULL:36   NULL:061   NULL:027100   NULL:1400000US36061027100  
##  NULL:36   NULL:061   NULL:027300   NULL:1400000US36061027300  
##  NULL:36   NULL:061   NULL:028500   NULL:1400000US36061028500  
##  NULL:36   NULL:061   NULL:029300   NULL:1400000US36061029300  
##  NULL:36   NULL:061   NULL:031900   NULL:1400000US36061031900  
##  NULL:36   NULL:061   NULL:000100   NULL:1400000US36061000100  
##  NULL:36   NULL:061   NULL:000600   NULL:1400000US36061000600  
##  NULL:36   NULL:061   NULL:000500   NULL:1400000US36061000500  
##  NULL:36   NULL:061   NULL:000800   NULL:1400000US36061000800  
##  NULL:36   NULL:061   NULL:001002   NULL:1400000US36061001002  
##  NULL:36   NULL:061   NULL:001402   NULL:1400000US36061001402  
##  NULL:36   NULL:061   NULL:001600   NULL:1400000US36061001600  
##  NULL:36   NULL:061   NULL:002202   NULL:1400000US36061002202  
##  NULL:36   NULL:061   NULL:002500   NULL:1400000US36061002500  
##  NULL:36   NULL:061   NULL:002700   NULL:1400000US36061002700  
##  NULL:36   NULL:061   NULL:002900   NULL:1400000US36061002900  
##  NULL:36   NULL:061   NULL:003300   NULL:1400000US36061003300  
##  NULL:36   NULL:061   NULL:003400   NULL:1400000US36061003400  
##  NULL:36   NULL:061   NULL:003800   NULL:1400000US36061003800  
##  NULL:36   NULL:061   NULL:004100   NULL:1400000US36061004100  
##  NULL:36   NULL:061   NULL:004400   NULL:1400000US36061004400  
##  NULL:36   NULL:061   NULL:005400   NULL:1400000US36061005400  
##  NULL:36   NULL:061   NULL:005502   NULL:1400000US36061005502  
##  NULL:36   NULL:061   NULL:005800   NULL:1400000US36061005800  
##  NULL:36   NULL:061   NULL:006000   NULL:1400000US36061006000  
##  NULL:36   NULL:061   NULL:008400   NULL:1400000US36061008400  
##  NULL:36   NULL:061   NULL:008602   NULL:1400000US36061008602  
##  NULL:36   NULL:061   NULL:008800   NULL:1400000US36061008800  
##  NULL:36   NULL:061   NULL:009300   NULL:1400000US36061009300  
##  NULL:36   NULL:061   NULL:009700   NULL:1400000US36061009700  
##  NULL:36   NULL:061   NULL:010000   NULL:1400000US36061010000  
##  NULL:36   NULL:061   NULL:010300   NULL:1400000US36061010300  
##  NULL:36   NULL:061   NULL:010601   NULL:1400000US36061010601  
##  NULL:36   NULL:061   NULL:011401   NULL:1400000US36061011401  
##  NULL:36   NULL:061   NULL:011600   NULL:1400000US36061011600  
##  NULL:36   NULL:061   NULL:011900   NULL:1400000US36061011900  
##  NULL:36   NULL:061   NULL:012400   NULL:1400000US36061012400  
##  NULL:36   NULL:061   NULL:012900   NULL:1400000US36061012900  
##  NULL:36   NULL:061   NULL:023700   NULL:1400000US36061023700  
##  NULL:36   NULL:061   NULL:023900   NULL:1400000US36061023900  
##  NULL:36   NULL:061   NULL:013200   NULL:1400000US36061013200  
##  NULL:36   NULL:061   NULL:028100   NULL:1400000US36061028100  
##  NULL:36   NULL:061   NULL:028300   NULL:1400000US36061028300  
##  NULL:36   NULL:061   NULL:029500   NULL:1400000US36061029500  
##  NULL:36   NULL:061   NULL:030300   NULL:1400000US36061030300  
##  NULL:36   NULL:061   NULL:031703   NULL:1400000US36061031703  
##  NULL:36   NULL:061   NULL:013500   NULL:1400000US36061013500  
##  NULL:36   NULL:061   NULL:013700   NULL:1400000US36061013700  
##  NULL:36   NULL:061   NULL:014300   NULL:1400000US36061014300  
##  NULL:36   NULL:061   NULL:014402   NULL:1400000US36061014402  
##  NULL:36   NULL:061   NULL:014602   NULL:1400000US36061014602  
##  NULL:36   NULL:061   NULL:015001   NULL:1400000US36061015001  
##  NULL:36   NULL:061   NULL:015100   NULL:1400000US36061015100  
##  NULL:36   NULL:061   NULL:015200   NULL:1400000US36061015200  
##  NULL:36   NULL:061   NULL:015601   NULL:1400000US36061015601  
##  NULL:36   NULL:061   NULL:015801   NULL:1400000US36061015801  
##  NULL:36   NULL:061   NULL:015900   NULL:1400000US36061015900  
##  NULL:36   NULL:061   NULL:016100   NULL:1400000US36061016100  
##  NULL:36   NULL:061   NULL:016600   NULL:1400000US36061016600  
##  NULL:36   NULL:061   NULL:017100   NULL:1400000US36061017100  
##  NULL:36   NULL:061   NULL:017200   NULL:1400000US36061017200  
##  NULL:36   NULL:061   NULL:017900   NULL:1400000US36061017900  
##  NULL:36   NULL:061   NULL:018200   NULL:1400000US36061018200  
##  NULL:36   NULL:061   NULL:018400   NULL:1400000US36061018400  
##  NULL:36   NULL:061   NULL:018900   NULL:1400000US36061018900  
##  NULL:36   NULL:061   NULL:019100   NULL:1400000US36061019100  
##  NULL:36   NULL:061   NULL:019400   NULL:1400000US36061019400  
##  NULL:36   NULL:061   NULL:019600   NULL:1400000US36061019600  
##  NULL:36   NULL:061   NULL:020000   NULL:1400000US36061020000  
##  NULL:36   NULL:061   NULL:020101   NULL:1400000US36061020101  
##  NULL:36   NULL:061   NULL:020600   NULL:1400000US36061020600  
##  NULL:36   NULL:061   NULL:020901   NULL:1400000US36061020901  
##  NULL:36   NULL:061   NULL:021100   NULL:1400000US36061021100  
##  NULL:36   NULL:061   NULL:021400   NULL:1400000US36061021400  
##  NULL:36   NULL:061   NULL:021703   NULL:1400000US36061021703  
##  NULL:36   NULL:061   NULL:022500   NULL:1400000US36061022500  
##  NULL:36   NULL:061   NULL:022800   NULL:1400000US36061022800  
##  NULL:36   NULL:061   NULL:023000   NULL:1400000US36061023000  
##  NULL:36   NULL:061   NULL:023502   NULL:1400000US36061023502  
##  NULL:36   NULL:061   NULL:000202   NULL:1400000US36061000202  
##  NULL:36   NULL:061   NULL:025500   NULL:1400000US36061025500  
##  NULL:36   NULL:061   NULL:027500   NULL:1400000US36061027500  
##  NULL:36   NULL:061   NULL:029700   NULL:1400000US36061029700  
##  NULL:36   NULL:061   NULL:001800   NULL:1400000US36061001800  
##  NULL:36   NULL:061   NULL:004200   NULL:1400000US36061004200  
##  NULL:36   NULL:061   NULL:005900   NULL:1400000US36061005900  
##  NULL:36   NULL:061   NULL:007200   NULL:1400000US36061007200  
##  NULL:36   NULL:061   NULL:007800   NULL:1400000US36061007800  
##  NULL:36   NULL:061   NULL:009400   NULL:1400000US36061009400  
##  NULL:36   NULL:061   NULL:011000   NULL:1400000US36061011000  
##  NULL:36   NULL:061   NULL:012000   NULL:1400000US36061012000  
##  NULL:36   NULL:061   NULL:013300   NULL:1400000US36061013300  
##  NULL:36   NULL:061   NULL:014601   NULL:1400000US36061014601  
##  NULL:36   NULL:061   NULL:017800   NULL:1400000US36061017800  
##  NULL:36   NULL:061   NULL:020102   NULL:1400000US36061020102  
##  NULL:36   NULL:061   NULL:021600   NULL:1400000US36061021600  
##  NULL:36   NULL:061   NULL:022302   NULL:1400000US36061022302  
##  NULL:36   NULL:061   NULL:022900   NULL:1400000US36061022900  
##  NULL:36   NULL:061   NULL:024000   NULL:1400000US36061024000  
##  NULL:36   NULL:061   NULL:024302   NULL:1400000US36061024302  
##  NULL:36   NULL:061   NULL:010400   NULL:1400000US36061010400  
##  NULL:36   NULL:061   NULL:025700   NULL:1400000US36061025700  
##  NULL:36   NULL:061   NULL:016001   NULL:1400000US36061016001  
##  NULL:36   NULL:061   NULL:004500   NULL:1400000US36061004500  
##  NULL:36   NULL:061   NULL:001200   NULL:1400000US36061001200  
##  NULL:36   NULL:061   NULL:006200   NULL:1400000US36061006200  
##  NULL:36   NULL:061   NULL:007300   NULL:1400000US36061007300  
##  NULL:36   NULL:061   NULL:007900   NULL:1400000US36061007900  
##  NULL:36   NULL:061   NULL:010800   NULL:1400000US36061010800  
##  NULL:36   NULL:061   NULL:012200   NULL:1400000US36061012200  
##  NULL:36   NULL:061   NULL:013800   NULL:1400000US36061013800  
##  NULL:36   NULL:061   NULL:016800   NULL:1400000US36061016800  
##  NULL:36   NULL:061   NULL:018300   NULL:1400000US36061018300  
##  NULL:36   NULL:061   NULL:022000   NULL:1400000US36061022000  
##  NULL:36   NULL:061   NULL:023400   NULL:1400000US36061023400  
##  NULL:36   NULL:061   NULL:026300   NULL:1400000US36061026300  
##  NULL:36   NULL:061   NULL:027700   NULL:1400000US36061027700  
##  NULL:36   NULL:061   NULL:031100   NULL:1400000US36061031100  
##  NULL:36   NULL:061   NULL:001501   NULL:1400000US36061001501  
##  NULL:36   NULL:061   NULL:009200   NULL:1400000US36061009200  
##  NULL:36   NULL:061   NULL:016200   NULL:1400000US36061016200  
##  NULL:36   NULL:061   NULL:021900   NULL:1400000US36061021900  
##  NULL:36   NULL:061   NULL:028700   NULL:1400000US36061028700  
##  NULL:36   NULL:061   NULL:002100   NULL:1400000US36061002100  
##  NULL:36   NULL:061   NULL:003900   NULL:1400000US36061003900  
##  NULL:36   NULL:061   NULL:005000   NULL:1400000US36061005000  
##  NULL:36   NULL:061   NULL:009000   NULL:1400000US36061009000  
##  NULL:36   NULL:061   NULL:011201   NULL:1400000US36061011201  
##  NULL:36   NULL:061   NULL:012100   NULL:1400000US36061012100  
##  NULL:36   NULL:061   NULL:013400   NULL:1400000US36061013400  
##  NULL:36   NULL:061   NULL:014802   NULL:1400000US36061014802  
##  NULL:36   NULL:061   NULL:015602   NULL:1400000US36061015602  
##  NULL:36   NULL:061   NULL:016700   NULL:1400000US36061016700  
##  NULL:36   NULL:061   NULL:018700   NULL:1400000US36061018700  
##  NULL:36   NULL:061   NULL:019701   NULL:1400000US36061019701  
##  NULL:36   NULL:061   NULL:022102   NULL:1400000US36061022102  
##  NULL:36   NULL:061   NULL:023802   NULL:1400000US36061023802  
##  NULL:36   NULL:061   NULL:015300   NULL:1400000US36061015300  
##  NULL:36   NULL:061   NULL:029900   NULL:1400000US36061029900  
##  NULL:36   NULL:061   NULL:011203   NULL:1400000US36061011203  
##  NULL:36   NULL:061   NULL:017500   NULL:1400000US36061017500  
##  NULL:36   NULL:061   NULL:018800   NULL:1400000US36061018800  
##  NULL:36   NULL:061   NULL:018000   NULL:1400000US36061018000  
##  NULL:36   NULL:061   NULL:021800   NULL:1400000US36061021800  
##  NULL:36   NULL:061   NULL:017700   NULL:1400000US36061017700  
##  NULL:36   NULL:061   NULL:004900   NULL:1400000US36061004900  
##  NULL:36   NULL:061   NULL:010900   NULL:1400000US36061010900  
##  NULL:36   NULL:061   NULL:015500   NULL:1400000US36061015500  
##  NULL:36   NULL:061   NULL:008603   NULL:1400000US36061008603  
##  NULL:36   NULL:061   NULL:023100   NULL:1400000US36061023100  
##  NULL:36   NULL:061   NULL:011202   NULL:1400000US36061011202  
##  NULL:36   NULL:061   NULL:012500   NULL:1400000US36061012500  
##  NULL:36   NULL:061   NULL:006800   NULL:1400000US36061006800  
##  NULL:36   NULL:061   NULL:019702   NULL:1400000US36061019702  
##  NULL:36   NULL:061   NULL:030700   NULL:1400000US36061030700  
##  NULL:36   NULL:061   NULL:002602   NULL:1400000US36061002602  
##  NULL:36   NULL:061   NULL:002601   NULL:1400000US36061002601  
##  NULL:36   NULL:061   NULL:011800   NULL:1400000US36061011800  
##  NULL:36   NULL:061   NULL:015400   NULL:1400000US36061015400  
##  NULL:36   NULL:061   NULL:009100   NULL:1400000US36061009100  
##  NULL:36   NULL:061   NULL:021000   NULL:1400000US36061021000  
##  NULL:36   NULL:061   NULL:027900   NULL:1400000US36061027900  
##   GEOID               NAME          LSAD     ALAND          AWATER       
##  NULL:36061001401   NULL:14.01    NULL:CT   NULL:93510     NULL:0        
##  NULL:36061002201   NULL:22.01    NULL:CT   NULL:161667    NULL:0        
##  NULL:36061003200   NULL:32       NULL:CT   NULL:217682    NULL:0        
##  NULL:36061004000   NULL:40       NULL:CT   NULL:178340    NULL:0        
##  NULL:36061005501   NULL:55.01    NULL:CT   NULL:124447    NULL:0        
##  NULL:36061005600   NULL:56       NULL:CT   NULL:174425    NULL:0        
##  NULL:36061007400   NULL:74       NULL:CT   NULL:174085    NULL:0        
##  NULL:36061007700   NULL:77       NULL:CT   NULL:178798    NULL:0        
##  NULL:36061008200   NULL:82       NULL:CT   NULL:172401    NULL:0        
##  NULL:36061010200   NULL:102      NULL:CT   NULL:172891    NULL:0        
##  NULL:36061011500   NULL:115      NULL:CT   NULL:175588    NULL:0        
##  NULL:36061012800   NULL:128      NULL:CT   NULL:178790    NULL:0        
##  NULL:36061014401   NULL:144.01   NULL:CT   NULL:74319     NULL:0        
##  NULL:36061015002   NULL:150.02   NULL:CT   NULL:125571    NULL:0        
##  NULL:36061015802   NULL:158.02   NULL:CT   NULL:51510     NULL:0        
##  NULL:36061017300   NULL:173      NULL:CT   NULL:178602    NULL:0        
##  NULL:36061018500   NULL:185      NULL:CT   NULL:130383    NULL:0        
##  NULL:36061018600   NULL:186      NULL:CT   NULL:121738    NULL:0        
##  NULL:36061022400   NULL:224      NULL:CT   NULL:194687    NULL:0        
##  NULL:36061023600   NULL:236      NULL:CT   NULL:263158    NULL:127979   
##  NULL:36061024900   NULL:249      NULL:CT   NULL:60659     NULL:0        
##  NULL:36061025300   NULL:253      NULL:CT   NULL:169349    NULL:0        
##  NULL:36061026700   NULL:267      NULL:CT   NULL:47338     NULL:0        
##  NULL:36061031704   NULL:317.04   NULL:CT   NULL:219944    NULL:473123   
##  NULL:36061001300   NULL:13       NULL:CT   NULL:312732    NULL:0        
##  NULL:36061002000   NULL:20       NULL:CT   NULL:126283    NULL:137883   
##  NULL:36061003100   NULL:31       NULL:CT   NULL:204969    NULL:0        
##  NULL:36061003602   NULL:36.02    NULL:CT   NULL:89011     NULL:0        
##  NULL:36061004300   NULL:43       NULL:CT   NULL:149239    NULL:0        
##  NULL:36061004700   NULL:47       NULL:CT   NULL:165421    NULL:0        
##  NULL:36061006300   NULL:63       NULL:CT   NULL:178187    NULL:0        
##  NULL:36061008700   NULL:87       NULL:CT   NULL:165443    NULL:0        
##  NULL:36061009500   NULL:95       NULL:CT   NULL:171964    NULL:0        
##  NULL:36061009800   NULL:98       NULL:CT   NULL:177068    NULL:0        
##  NULL:36061010602   NULL:106.02   NULL:CT   NULL:125711    NULL:73073    
##  NULL:36061011100   NULL:111      NULL:CT   NULL:175465    NULL:0        
##  NULL:36061012700   NULL:127      NULL:CT   NULL:174026    NULL:0        
##  NULL:36061013000   NULL:130      NULL:CT   NULL:178226    NULL:0        
##  NULL:36061013900   NULL:139      NULL:CT   NULL:181595    NULL:0        
##  NULL:36061014500   NULL:145      NULL:CT   NULL:178321    NULL:0        
##  NULL:36061014801   NULL:148.01   NULL:CT   NULL:51979     NULL:0        
##  NULL:36061016400   NULL:164      NULL:CT   NULL:177390    NULL:0        
##  NULL:36061016900   NULL:169      NULL:CT   NULL:180002    NULL:0        
##  NULL:36061017401   NULL:174.01   NULL:CT   NULL:100812    NULL:0        
##  NULL:36061018100   NULL:181      NULL:CT   NULL:138455    NULL:0        
##  NULL:36061019200   NULL:192      NULL:CT   NULL:189854    NULL:175645   
##  NULL:36061019800   NULL:198      NULL:CT   NULL:225452    NULL:0        
##  NULL:36061020300   NULL:203      NULL:CT   NULL:178223    NULL:0        
##  NULL:36061021200   NULL:212      NULL:CT   NULL:176416    NULL:0        
##  NULL:36061022200   NULL:222      NULL:CT   NULL:181422    NULL:0        
##  NULL:36061022600   NULL:226      NULL:CT   NULL:152979    NULL:0        
##  NULL:36061023501   NULL:235.01   NULL:CT   NULL:179045    NULL:0        
##  NULL:36061024100   NULL:241      NULL:CT   NULL:178781    NULL:207330   
##  NULL:36061029100   NULL:291      NULL:CT   NULL:187479    NULL:0        
##  NULL:36061030900   NULL:309      NULL:CT   NULL:302752    NULL:0        
##  NULL:36061000201   NULL:2.01     NULL:CT   NULL:90233     NULL:75976    
##  NULL:36061003002   NULL:30.02    NULL:CT   NULL:78525     NULL:0        
##  NULL:36061006700   NULL:67       NULL:CT   NULL:168478    NULL:0        
##  NULL:36061008300   NULL:83       NULL:CT   NULL:174158    NULL:0        
##  NULL:36061011402   NULL:114.02   NULL:CT   NULL:98799     NULL:0        
##  NULL:36061016500   NULL:165      NULL:CT   NULL:204317    NULL:0        
##  NULL:36061019000   NULL:190      NULL:CT   NULL:103802    NULL:0        
##  NULL:36061021500   NULL:215      NULL:CT   NULL:69710     NULL:0        
##  NULL:36061023300   NULL:233      NULL:CT   NULL:232212    NULL:285722   
##  NULL:36061026500   NULL:265      NULL:CT   NULL:195037    NULL:0        
##  NULL:36061006500   NULL:65       NULL:CT   NULL:201131    NULL:0        
##  NULL:36061020500   NULL:205      NULL:CT   NULL:281121    NULL:374540   
##  NULL:36061024700   NULL:247      NULL:CT   NULL:256348    NULL:400307   
##  NULL:36061001001   NULL:10.01    NULL:CT   NULL:80145     NULL:244297   
##  NULL:36061003001   NULL:30.01    NULL:CT   NULL:124153    NULL:0        
##  NULL:36061006100   NULL:61       NULL:CT   NULL:142017    NULL:0        
##  NULL:36061010100   NULL:101      NULL:CT   NULL:175518    NULL:0        
##  NULL:36061014000   NULL:140      NULL:CT   NULL:179108    NULL:0        
##  NULL:36061020701   NULL:207.01   NULL:CT   NULL:47273     NULL:0        
##  NULL:36061000700   NULL:7        NULL:CT   NULL:256720    NULL:242749   
##  NULL:36061000900   NULL:9        NULL:CT   NULL:315105    NULL:460146   
##  NULL:36061001502   NULL:15.02    NULL:CT   NULL:207277    NULL:146802   
##  NULL:36061002400   NULL:24       NULL:CT   NULL:182345    NULL:470253   
##  NULL:36061002800   NULL:28       NULL:CT   NULL:183616    NULL:0        
##  NULL:36061003601   NULL:36.01    NULL:CT   NULL:99812     NULL:0        
##  NULL:36061003700   NULL:37       NULL:CT   NULL:383376    NULL:322356   
##  NULL:36061004800   NULL:48       NULL:CT   NULL:175756    NULL:0        
##  NULL:36061005200   NULL:52       NULL:CT   NULL:168892    NULL:0        
##  NULL:36061005700   NULL:57       NULL:CT   NULL:108054    NULL:0        
##  NULL:36061006400   NULL:64       NULL:CT   NULL:179196    NULL:0        
##  NULL:36061006600   NULL:66       NULL:CT   NULL:174704    NULL:0        
##  NULL:36061006900   NULL:69       NULL:CT   NULL:220486    NULL:275547   
##  NULL:36061007000   NULL:70       NULL:CT   NULL:177329    NULL:0        
##  NULL:36061007100   NULL:71       NULL:CT   NULL:164996    NULL:0        
##  NULL:36061007500   NULL:75       NULL:CT   NULL:165732    NULL:283835   
##  NULL:36061007600   NULL:76       NULL:CT   NULL:174274    NULL:0        
##  NULL:36061008000   NULL:80       NULL:CT   NULL:172626    NULL:0        
##  NULL:36061008100   NULL:81       NULL:CT   NULL:174287    NULL:0        
##  NULL:36061008601   NULL:86.01    NULL:CT   NULL:125340    NULL:499842   
##  NULL:36061008900   NULL:89       NULL:CT   NULL:165578    NULL:0        
##  NULL:36061009600   NULL:96       NULL:CT   NULL:175329    NULL:0        
##  NULL:36061009900   NULL:99       NULL:CT   NULL:1002374   NULL:1130336  
##  NULL:36061011700   NULL:117      NULL:CT   NULL:187347    NULL:176830   
##  NULL:36061012600   NULL:126      NULL:CT   NULL:183653    NULL:0        
##  NULL:36061013100   NULL:131      NULL:CT   NULL:174140    NULL:0        
##  NULL:36061013600   NULL:136      NULL:CT   NULL:219496    NULL:111000   
##  NULL:36061014200   NULL:142      NULL:CT   NULL:178987    NULL:0        
##  NULL:36061014700   NULL:147      NULL:CT   NULL:65414     NULL:0        
##  NULL:36061014900   NULL:149      NULL:CT   NULL:174047    NULL:0        
##  NULL:36061015700   NULL:157      NULL:CT   NULL:182396    NULL:0        
##  NULL:36061016002   NULL:160.02   NULL:CT   NULL:51424     NULL:0        
##  NULL:36061016300   NULL:163      NULL:CT   NULL:263616    NULL:352996   
##  NULL:36061017000   NULL:170      NULL:CT   NULL:182916    NULL:0        
##  NULL:36061017402   NULL:174.02   NULL:CT   NULL:50730     NULL:0        
##  NULL:36061019300   NULL:193      NULL:CT   NULL:175716    NULL:0        
##  NULL:36061019500   NULL:195      NULL:CT   NULL:218436    NULL:166301   
##  NULL:36061019900   NULL:199      NULL:CT   NULL:226590    NULL:192056   
##  NULL:36061020800   NULL:208      NULL:CT   NULL:148566    NULL:0        
##  NULL:36061011300   NULL:113      NULL:CT   NULL:175653    NULL:0        
##  NULL:36061021303   NULL:213.03   NULL:CT   NULL:153292    NULL:0        
##  NULL:36061022301   NULL:223.01   NULL:CT   NULL:186804    NULL:131253   
##  NULL:36061022700   NULL:227      NULL:CT   NULL:150703    NULL:0        
##  NULL:36061023200   NULL:232      NULL:CT   NULL:180486    NULL:0        
##  NULL:36061023801   NULL:238.01   NULL:CT   NULL:352467    NULL:518659   
##  NULL:36061024200   NULL:242      NULL:CT   NULL:347358    NULL:135920   
##  NULL:36061024301   NULL:243.01   NULL:CT   NULL:105194    NULL:0        
##  NULL:36061024500   NULL:245      NULL:CT   NULL:212070    NULL:0        
##  NULL:36061025100   NULL:251      NULL:CT   NULL:159422    NULL:0        
##  NULL:36061025900   NULL:259      NULL:CT   NULL:75469     NULL:0        
##  NULL:36061026100   NULL:261      NULL:CT   NULL:156222    NULL:0        
##  NULL:36061026900   NULL:269      NULL:CT   NULL:175570    NULL:0        
##  NULL:36061027100   NULL:271      NULL:CT   NULL:192909    NULL:0        
##  NULL:36061027300   NULL:273      NULL:CT   NULL:176946    NULL:0        
##  NULL:36061028500   NULL:285      NULL:CT   NULL:121942    NULL:0        
##  NULL:36061029300   NULL:293      NULL:CT   NULL:157076    NULL:0        
##  NULL:36061031900   NULL:319      NULL:CT   NULL:103141    NULL:314424   
##  NULL:36061000100   NULL:1        NULL:CT   NULL:78638     NULL:0        
##  NULL:36061000600   NULL:6        NULL:CT   NULL:240406    NULL:176018   
##  NULL:36061000500   NULL:5        NULL:CT   NULL:843918    NULL:3393360  
##  NULL:36061000800   NULL:8        NULL:CT   NULL:220708    NULL:167906   
##  NULL:36061001002   NULL:10.02    NULL:CT   NULL:199683    NULL:245138   
##  NULL:36061001402   NULL:14.02    NULL:CT   NULL:113920    NULL:0        
##  NULL:36061001600   NULL:16       NULL:CT   NULL:207377    NULL:0        
##  NULL:36061002202   NULL:22.02    NULL:CT   NULL:56112     NULL:0        
##  NULL:36061002500   NULL:25       NULL:CT   NULL:131517    NULL:94115    
##  NULL:36061002700   NULL:27       NULL:CT   NULL:59282     NULL:0        
##  NULL:36061002900   NULL:29       NULL:CT   NULL:273357    NULL:0        
##  NULL:36061003300   NULL:33       NULL:CT   NULL:341677    NULL:0        
##  NULL:36061003400   NULL:34       NULL:CT   NULL:160257    NULL:0        
##  NULL:36061003800   NULL:38       NULL:CT   NULL:202371    NULL:0        
##  NULL:36061004100   NULL:41       NULL:CT   NULL:191275    NULL:0        
##  NULL:36061004400   NULL:44       NULL:CT   NULL:410012    NULL:465240   
##  NULL:36061005400   NULL:54       NULL:CT   NULL:167959    NULL:0        
##  NULL:36061005502   NULL:55.02    NULL:CT   NULL:115486    NULL:0        
##  NULL:36061005800   NULL:58       NULL:CT   NULL:174669    NULL:0        
##  NULL:36061006000   NULL:60       NULL:CT   NULL:118942    NULL:300815   
##  NULL:36061008400   NULL:84       NULL:CT   NULL:173067    NULL:0        
##  NULL:36061008602   NULL:86.02    NULL:CT   NULL:106753    NULL:317001   
##  NULL:36061008800   NULL:88       NULL:CT   NULL:179256    NULL:0        
##  NULL:36061009300   NULL:93       NULL:CT   NULL:179101    NULL:0        
##  NULL:36061009700   NULL:97       NULL:CT   NULL:172931    NULL:0        
##  NULL:36061010000   NULL:100      NULL:CT   NULL:172894    NULL:0        
##  NULL:36061010300   NULL:103      NULL:CT   NULL:174470    NULL:0        
##  NULL:36061010601   NULL:106.01   NULL:CT   NULL:121208    NULL:130046   
##  NULL:36061011401   NULL:114.01   NULL:CT   NULL:98508     NULL:0        
##  NULL:36061011600   NULL:116      NULL:CT   NULL:195582    NULL:132923   
##  NULL:36061011900   NULL:119      NULL:CT   NULL:177591    NULL:0        
##  NULL:36061012400   NULL:124      NULL:CT   NULL:171580    NULL:75103    
##  NULL:36061012900   NULL:129      NULL:CT   NULL:404224    NULL:407508   
##  NULL:36061023700   NULL:237      NULL:CT   NULL:271911    NULL:273621   
##  NULL:36061023900   NULL:239      NULL:CT   NULL:63850     NULL:0        
##  NULL:36061013200   NULL:132      NULL:CT   NULL:200886    NULL:92937    
##  NULL:36061028100   NULL:281      NULL:CT   NULL:94227     NULL:0        
##  NULL:36061028300   NULL:283      NULL:CT   NULL:151093    NULL:0        
##  NULL:36061029500   NULL:295      NULL:CT   NULL:135565    NULL:0        
##  NULL:36061030300   NULL:303      NULL:CT   NULL:156650    NULL:0        
##  NULL:36061031703   NULL:317.03   NULL:CT   NULL:258090    NULL:467423   
##  NULL:36061013500   NULL:135      NULL:CT   NULL:432329    NULL:401309   
##  NULL:36061013700   NULL:137      NULL:CT   NULL:225671    NULL:0        
##  NULL:36061014300   NULL:143      NULL:CT   NULL:2991654   NULL:567106   
##  NULL:36061014402   NULL:144.02   NULL:CT   NULL:176863    NULL:0        
##  NULL:36061014602   NULL:146.02   NULL:CT   NULL:109193    NULL:0        
##  NULL:36061015001   NULL:150.01   NULL:CT   NULL:51643     NULL:0        
##  NULL:36061015100   NULL:151      NULL:CT   NULL:333044    NULL:306023   
##  NULL:36061015200   NULL:152      NULL:CT   NULL:189341    NULL:529192   
##  NULL:36061015601   NULL:156.01   NULL:CT   NULL:73566     NULL:0        
##  NULL:36061015801   NULL:158.01   NULL:CT   NULL:125754    NULL:0        
##  NULL:36061015900   NULL:159      NULL:CT   NULL:175127    NULL:110770   
##  NULL:36061016100   NULL:161      NULL:CT   NULL:155741    NULL:0        
##  NULL:36061016600   NULL:166      NULL:CT   NULL:174218    NULL:0        
##  NULL:36061017100   NULL:171      NULL:CT   NULL:217835    NULL:194050   
##  NULL:36061017200   NULL:172      NULL:CT   NULL:177939    NULL:0        
##  NULL:36061017900   NULL:179      NULL:CT   NULL:213916    NULL:190568   
##  NULL:36061018200   NULL:182      NULL:CT   NULL:176899    NULL:0        
##  NULL:36061018400   NULL:184      NULL:CT   NULL:176580    NULL:0        
##  NULL:36061018900   NULL:189      NULL:CT   NULL:265399    NULL:0        
##  NULL:36061019100   NULL:191      NULL:CT   NULL:223476    NULL:175654   
##  NULL:36061019400   NULL:194      NULL:CT   NULL:175052    NULL:0        
##  NULL:36061019600   NULL:196      NULL:CT   NULL:177186    NULL:0        
##  NULL:36061020000   NULL:200      NULL:CT   NULL:129287    NULL:0        
##  NULL:36061020101   NULL:201.01   NULL:CT   NULL:55127     NULL:0        
##  NULL:36061020600   NULL:206      NULL:CT   NULL:148748    NULL:0        
##  NULL:36061020901   NULL:209.01   NULL:CT   NULL:115805    NULL:0        
##  NULL:36061021100   NULL:211      NULL:CT   NULL:347999    NULL:307945   
##  NULL:36061021400   NULL:214      NULL:CT   NULL:135562    NULL:0        
##  NULL:36061021703   NULL:217.03   NULL:CT   NULL:313864    NULL:0        
##  NULL:36061022500   NULL:225      NULL:CT   NULL:262466    NULL:136149   
##  NULL:36061022800   NULL:228      NULL:CT   NULL:180634    NULL:0        
##  NULL:36061023000   NULL:230      NULL:CT   NULL:173684    NULL:0        
##  NULL:36061023502   NULL:235.02   NULL:CT   NULL:41338     NULL:0        
##  NULL:36061000202   NULL:2.02     NULL:CT   NULL:310039    NULL:428737   
##  NULL:36061025500   NULL:255      NULL:CT   NULL:441356    NULL:586786   
##  NULL:36061027500   NULL:275      NULL:CT   NULL:279689    NULL:852909   
##  NULL:36061029700   NULL:297      NULL:CT   NULL:948118    NULL:1006331  
##  NULL:36061001800   NULL:18       NULL:CT   NULL:222968    NULL:0        
##  NULL:36061004200   NULL:42       NULL:CT   NULL:85723     NULL:0        
##  NULL:36061005900   NULL:59       NULL:CT   NULL:116172    NULL:0        
##  NULL:36061007200   NULL:72       NULL:CT   NULL:175015    NULL:0        
##  NULL:36061007800   NULL:78       NULL:CT   NULL:177474    NULL:0        
##  NULL:36061009400   NULL:94       NULL:CT   NULL:152754    NULL:0        
##  NULL:36061011000   NULL:110      NULL:CT   NULL:176509    NULL:0        
##  NULL:36061012000   NULL:120      NULL:CT   NULL:173024    NULL:0        
##  NULL:36061013300   NULL:133      NULL:CT   NULL:174356    NULL:0        
##  NULL:36061014601   NULL:146.01   NULL:CT   NULL:74328     NULL:0        
##  NULL:36061017800   NULL:178      NULL:CT   NULL:248851    NULL:209809   
##  NULL:36061020102   NULL:201.02   NULL:CT   NULL:83350     NULL:0        
##  NULL:36061021600   NULL:216      NULL:CT   NULL:173198    NULL:0        
##  NULL:36061022302   NULL:223.02   NULL:CT   NULL:60095     NULL:96898    
##  NULL:36061022900   NULL:229      NULL:CT   NULL:211053    NULL:131446   
##  NULL:36061024000   NULL:240      NULL:CT   NULL:2183933   NULL:793007   
##  NULL:36061024302   NULL:243.02   NULL:CT   NULL:213258    NULL:113258   
##  NULL:36061010400   NULL:104      NULL:CT   NULL:173232    NULL:0        
##  NULL:36061025700   NULL:257      NULL:CT   NULL:144063    NULL:0        
##  NULL:36061016001   NULL:160.01   NULL:CT   NULL:125615    NULL:0        
##  NULL:36061004500   NULL:45       NULL:CT   NULL:104947    NULL:0        
##  NULL:36061001200   NULL:12       NULL:CT   NULL:120597    NULL:0        
##  NULL:36061006200   NULL:62       NULL:CT   NULL:293510    NULL:862281   
##  NULL:36061007300   NULL:73       NULL:CT   NULL:165299    NULL:0        
##  NULL:36061007900   NULL:79       NULL:CT   NULL:285956    NULL:502030   
##  NULL:36061010800   NULL:108      NULL:CT   NULL:182589    NULL:0        
##  NULL:36061012200   NULL:122      NULL:CT   NULL:172616    NULL:0        
##  NULL:36061013800   NULL:138      NULL:CT   NULL:182091    NULL:0        
##  NULL:36061016800   NULL:168      NULL:CT   NULL:199278    NULL:0        
##  NULL:36061018300   NULL:183      NULL:CT   NULL:224312    NULL:187898   
##  NULL:36061022000   NULL:220      NULL:CT   NULL:174955    NULL:0        
##  NULL:36061023400   NULL:234      NULL:CT   NULL:108235    NULL:0        
##  NULL:36061026300   NULL:263      NULL:CT   NULL:157201    NULL:0        
##  NULL:36061027700   NULL:277      NULL:CT   NULL:163262    NULL:0        
##  NULL:36061031100   NULL:311      NULL:CT   NULL:730774    NULL:312736   
##  NULL:36061001501   NULL:15.01    NULL:CT   NULL:257851    NULL:175058   
##  NULL:36061009200   NULL:92       NULL:CT   NULL:197023    NULL:0        
##  NULL:36061016200   NULL:162      NULL:CT   NULL:272018    NULL:832556   
##  NULL:36061021900   NULL:219      NULL:CT   NULL:279408    NULL:191783   
##  NULL:36061028700   NULL:287      NULL:CT   NULL:532366    NULL:1018817  
##  NULL:36061002100   NULL:21       NULL:CT   NULL:236794    NULL:0        
##  NULL:36061003900   NULL:39       NULL:CT   NULL:331072    NULL:447417   
##  NULL:36061005000   NULL:50       NULL:CT   NULL:166467    NULL:0        
##  NULL:36061009000   NULL:90       NULL:CT   NULL:174692    NULL:0        
##  NULL:36061011201   NULL:112.01   NULL:CT   NULL:79536     NULL:0        
##  NULL:36061012100   NULL:121      NULL:CT   NULL:178161    NULL:0        
##  NULL:36061013400   NULL:134      NULL:CT   NULL:181779    NULL:0        
##  NULL:36061014802   NULL:148.02   NULL:CT   NULL:125620    NULL:0        
##  NULL:36061015602   NULL:156.02   NULL:CT   NULL:108755    NULL:0        
##  NULL:36061016700   NULL:167      NULL:CT   NULL:196978    NULL:141457   
##  NULL:36061018700   NULL:187      NULL:CT   NULL:218107    NULL:184467   
##  NULL:36061019701   NULL:197.01   NULL:CT   NULL:235976    NULL:0        
##  NULL:36061022102   NULL:221.02   NULL:CT   NULL:99000     NULL:0        
##  NULL:36061023802   NULL:238.02   NULL:CT   NULL:243417    NULL:313651   
##  NULL:36061015300   NULL:153      NULL:CT   NULL:174116    NULL:0        
##  NULL:36061029900   NULL:299      NULL:CT   NULL:616054    NULL:322917   
##  NULL:36061011203   NULL:112.03   NULL:CT   NULL:77947     NULL:0        
##  NULL:36061017500   NULL:175      NULL:CT   NULL:216905    NULL:192184   
##  NULL:36061018800   NULL:188      NULL:CT   NULL:148532    NULL:0        
##  NULL:36061018000   NULL:180      NULL:CT   NULL:212522    NULL:0        
##  NULL:36061021800   NULL:218      NULL:CT   NULL:181869    NULL:0        
##  NULL:36061017700   NULL:177      NULL:CT   NULL:174561    NULL:0        
##  NULL:36061004900   NULL:49       NULL:CT   NULL:207320    NULL:0        
##  NULL:36061010900   NULL:109      NULL:CT   NULL:175741    NULL:0        
##  NULL:36061015500   NULL:155      NULL:CT   NULL:197390    NULL:214654   
##  NULL:36061008603   NULL:86.03    NULL:CT   NULL:116670    NULL:153941   
##  NULL:36061023100   NULL:231      NULL:CT   NULL:182674    NULL:0        
##  NULL:36061011202   NULL:112.02   NULL:CT   NULL:78008     NULL:0        
##  NULL:36061012500   NULL:125      NULL:CT   NULL:174253    NULL:0        
##  NULL:36061006800   NULL:68       NULL:CT   NULL:174443    NULL:0        
##  NULL:36061019702   NULL:197.02   NULL:CT   NULL:47396     NULL:0        
##  NULL:36061030700   NULL:307      NULL:CT   NULL:84374     NULL:0        
##  NULL:36061002602   NULL:26.02    NULL:CT   NULL:103692    NULL:0        
##  NULL:36061002601   NULL:26.01    NULL:CT   NULL:106047    NULL:0        
##  NULL:36061011800   NULL:118      NULL:CT   NULL:176768    NULL:0        
##  NULL:36061015400   NULL:154      NULL:CT   NULL:177371    NULL:0        
##  NULL:36061009100   NULL:91       NULL:CT   NULL:179099    NULL:0        
##  NULL:36061021000   NULL:210      NULL:CT   NULL:203716    NULL:116287   
##  NULL:36061027900   NULL:279      NULL:CT   NULL:180013    NULL:0

# Plot nyc_tracts
tigris::plot(nyc_tracts)

# proj4string() on nyc_tracts and neighborhoods
proj4string(nyc_tracts)

## [1] "+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0"

proj4string(neighborhoods)

## [1] "+proj=lcc +lat_1=40.66666666666666 +lat_2=41.03333333333333 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000 +y_0=0 +datum=NAD83 +units=us-ft +no_defs +ellps=GRS80 +towgs84=0,0,0"

# coordinates() on nyc_tracts and neighborhoods
head(coordinates(nyc_tracts))

##          [,1]     [,2]
## 156 -73.98733 40.71516
## 157 -73.98184 40.71912
## 158 -73.98372 40.72556
## 159 -73.98603 40.73074
## 160 -73.99708 40.72762
## 161 -73.98730 40.74179

head(coordinates(neighborhoods))

##        [,1]     [,2]
## 0  987397.5 169148.4
## 1 1037640.2 214077.6
## 2 1043002.7 212969.8
## 3 1037005.2 219265.3
## 4 1020706.7 217413.9
## 5 1011274.1 240777.4

# plot() neighborhoods and nyc_tracts
plot(neighborhoods)
plot(nyc_tracts, col="red", add=TRUE)

# Use spTransform on neighborhoods: neighborhoods
neighborhoods <- sp::spTransform(neighborhoods, proj4string(nyc_tracts))

# head() on coordinates() of neighborhoods
head(coordinates(neighborhoods))

##        [,1]     [,2]
## 0 -73.98866 40.63095
## 1 -73.80729 40.75411
## 2 -73.78795 40.75104
## 3 -73.80955 40.76835
## 4 -73.86840 40.76335
## 5 -73.90235 40.82751

## *** NOTE THAT I DO NOT HAVE THE WATER DATA
# Plot neighborhoods, nyc_tracts and water
plot(neighborhoods)
plot(nyc_tracts, col="red", add=TRUE)

# plot(water, col="blue", add=TRUE)

tct_label <- "000100\n000201\n000202\n000500\n000600\n000700\n000800\n000900\n001001\n001002\n001200\n001300\n001401\n001402\n001501\n001502\n001600\n001800\n002000\n002100\n002201\n002202\n002400\n002500\n002601\n002602\n002700\n002800\n002900\n003001\n003002\n003100\n003200\n003300\n003400\n003601\n003602\n003700\n003800\n003900\n004000\n004100\n004200\n004300\n004400\n004500\n004700\n004800\n004900\n005000\n005200\n005400\n005501\n005502\n005600\n005700\n005800\n005900\n006000\n006100\n006200\n006300\n006400\n006500\n006600\n006700\n006800\n006900\n007000\n007100\n007200\n007300\n007400\n007500\n007600\n007700\n007800\n007900\n008000\n008100\n008200\n008300\n008400\n008601\n008602\n008603\n008700\n008800\n008900\n009000\n009100\n009200\n009300\n009400\n009500\n009600\n009700\n009800\n009900\n010000\n010100\n010200\n010300\n010400\n010601\n010602\n010800\n010900\n011000\n011100\n011201\n011202\n011203\n011300\n011401\n011402\n011500\n011600\n011700\n011800\n011900\n012000\n012100\n012200\n012400\n012500\n012600\n012700\n012800\n012900\n013000\n013100\n013200\n013300\n013400\n013500\n013600\n013700\n013800\n013900\n014000\n014200\n014300\n014401\n014402\n014500\n014601\n014602\n014700\n014801\n014802\n014900\n015001\n015002\n015100\n015200\n015300\n015400\n015500\n015601\n015602\n015700\n015801\n015802\n015900\n016001\n016002\n016100\n016200\n016300\n016400\n016500\n016600\n016700\n016800\n016900\n017000\n017100\n017200\n017300\n017401\n017402\n017500\n017700\n017800\n017900\n018000\n018100\n018200\n018300\n018400\n018500\n018600\n018700\n018800\n018900\n019000\n019100\n019200\n019300\n019400\n019500\n019600\n019701\n019702\n019800\n019900\n020000\n020101\n020102\n020300\n020500\n020600\n020701\n020800\n020901\n021000\n021100\n021200\n021303\n021400\n021500\n021600\n021703\n021800\n021900\n022000\n022102\n022200\n022301\n022302\n022400\n022500\n022600\n022700\n022800\n022900\n023000\n023100\n023200\n023300\n023400\n023501\n023502\n023600\n023700\n023801\n023802\n023900\n024000\n024100\n024200\n024301\n024302\n024500\n024700\n024900\n025100\n025300\n025500\n025700\n025900\n026100\n026300\n026500\n026700\n026900\n027100\n027300\n027500\n027700\n027900\n028100\n028300\n028500\n028700\n029100\n029300\n029500\n029700\n029900\n030300\n030700\n030900\n031100\n031703\n031704\n031900\n"

tct_fam_inc <- "0 \n22165 \n49622 \n0 \n24532 \n187039 \n33686 \n266885 \n120647 \n27036 \n59525 \n214293 \n161107 \n37658 \n83425 \n187439 \n29004 \n41404 \n21192 \n266885 \n44032 \n137345 \n23174 \n21206 \n35944 \n60454 \n84623 \n37273 \n35930 \n52519 \n64716 \n102852 \n70342 \n266885 \n79758 \n25192 \n74662 \n184448 \n93436 \n123318 \n114759 \n39734 \n194655 \n47168 \n108036 \n124277 \n168309 \n128103 \n266885 \n193827 \n208166 \n206017 \n153084 \n135014 \n206721 \n127224 \n266885 \n202968 \n161484 \n236153 \n137446 \n266885 \n127072 \n136728 \n82125 \n169819 \n117547 \n174008 \n193559 \n240669 \n151095 \n133090 \n173558 \n195454 \n77878 \n160642 \n206932 \n168940 \n178727 \n151146 \n140401 \n29948 \n167299 \n145403 \n0 \n256677 \n184949 \n153847 \n83621 \n207171 \n231572 \n163655 \n93110 \n0 \n162130 \n0 \n49252 \n211259 \n266885 \n171053 \n81668 \n45019 \n102751 \n100805 \n180498 \n219695 \n219782 \n266885 \n194045 \n116596 \n176679 \n266885 \n139711 \n0 \n235914 \n220181 \n87178 \n149984 \n68135 \n222882 \n33708 \n238636 \n76883 \n242252 \n148256 \n204064 \n190444 \n54828 \n259944 \n101553 \n266885 \n222846 \n156649 \n93596 \n174741 \n41128 \n191127 \n189566 \n153418 \n153026 \n254819 \n266885 \n0 \n214460 \n220341 \n221031 \n177667 \n219114 \n184593 \n233823 \n194612 \n265992 \n266885 \n250804 \n59002 \n90053 \n266885 \n128625 \n216914 \n110141 \n32467 \n220261 \n266885 \n102751 \n223550 \n266885 \n177747 \n216566 \n37077 \n218178 \n34362 \n232429 \n34049 \n212174 \n26151 \n179562 \n51205 \n190089 \n36133 \n216631 \n30819 \n28684 \n208798 \n88550 \n36845 \n161194 \n23682 \n120218 \n26724 \n128865 \n33040 \n175968 \n20488 \n141839 \n39023 \n42152 \n80368 \n127057 \n21330 \n50551 \n18208 \n122382 \n35458 \n111499 \n58922 \n84347 \n119529 \n57093 \n129141 \n50842 \n44954 \n167866 \n40562 \n69747 \n47938 \n28292 \n45041 \n66233 \n75410 \n55655 \n50515 \n36373 \n47568 \n0 \n47030 \n22194 \n49216 \n72404 \n37737 \n31320 \n21315 \n25497 \n37186 \n43524 \n52555 \n45244 \n38906 \n34100 \n42660 \n29998 \n32844 \n42159 \n41796 \n40525 \n45600 \n36982 \n86372 \n177253 \n38006 \n0 \n58283 \n36888 \n38558 \n24227 \n35835 \n37600 \n51524 \n34732 \n40794 \n49317 \n59380 \n49419 \n40257 \n26739 \n51735 \n41629 \n32067 \n44765 \n123696 \n113692 \n33730 \n39444 \n123747 \n46863 \n29011 \n37825 \n33926 \n38507 \n53267 \n0 \n30129 \n42028 \n87955 \n37360 \n0 \n216435 \n180593 \n0 \n"


nyc_income <- data.frame(tract=strsplit(tct_label, "\n")[[1]], 
                         estimate=as.numeric(strsplit(tct_fam_inc, "\n")[[1]]), 
                         stringsAsFactors=FALSE
                         )

# Example for how to screw up the data slot by assuming same orders!
# Use str() on nyc_income and nyc_tracts@data
str(nyc_income)

## 'data.frame':    288 obs. of  2 variables:
##  $ tract   : chr  "000100" "000201" "000202" "000500" ...
##  $ estimate: num  0 22165 49622 0 24532 ...

str(nyc_tracts@data)

## 'data.frame':    288 obs. of  9 variables:
##  $ STATEFP : chr  "36" "36" "36" "36" ...
##  $ COUNTYFP: chr  "061" "061" "061" "061" ...
##  $ TRACTCE : chr  "001401" "002201" "003200" "004000" ...
##  $ AFFGEOID: chr  "1400000US36061001401" "1400000US36061002201" "1400000US36061003200" "1400000US36061004000" ...
##  $ GEOID   : chr  "36061001401" "36061002201" "36061003200" "36061004000" ...
##  $ NAME    : chr  "14.01" "22.01" "32" "40" ...
##  $ LSAD    : chr  "CT" "CT" "CT" "CT" ...
##  $ ALAND   : chr  "93510" "161667" "217682" "178340" ...
##  $ AWATER  : chr  "0" "0" "0" "0" ...

# Highlight tract 002201 in nyc_tracts
plot(nyc_tracts)
plot(nyc_tracts[nyc_tracts$TRACTCE == "002201", ],
     col = "red", add = TRUE)

# Set nyc_tracts@data to nyc_income
nyc_dataSave <- nyc_tracts@data
nyc_tracts@data <- nyc_income

# Highlight tract 002201 in nyc_tracts
plot(nyc_tracts)
plot(nyc_tracts[nyc_tracts$tract == "002201", ],
     col = "red", add = TRUE)

nyc_tracts@data <- nyc_dataSave

# Better approach - first, verify that the data can be matched up without errors
# Check for duplicates in nyc_income
any(duplicated(nyc_income$tract))

## [1] FALSE

# Check for duplicates in nyc_tracts
any(duplicated(nyc_tracts$TRACTCE))

## [1] FALSE

# Check nyc_tracts in nyc_income
all(nyc_tracts$TRACTCE %in% nyc_income$tract)

## [1] TRUE

# Check nyc_income in nyc_tracts
all(nyc_income$tract %in% nyc_tracts$TRACTCE)

## [1] TRUE

# Merge nyc_tracts and nyc_income: nyc_tracts_merge
nyc_tracts_merge <- sp::merge(nyc_tracts, nyc_income, by.x="TRACTCE", by.y="tract")

# Call summary() on nyc_tracts_merge
summary(nyc_tracts_merge)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##         min       max
## x -74.04731 -73.90700
## y  40.68419  40.88207
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0]
## Data attributes:
##  TRACTCE       STATEFP   COUNTYFP   AFFGEOID                   
##  NULL:001401   NULL:36   NULL:061   NULL:1400000US36061001401  
##  NULL:002201   NULL:36   NULL:061   NULL:1400000US36061002201  
##  NULL:003200   NULL:36   NULL:061   NULL:1400000US36061003200  
##  NULL:004000   NULL:36   NULL:061   NULL:1400000US36061004000  
##  NULL:005501   NULL:36   NULL:061   NULL:1400000US36061005501  
##  NULL:005600   NULL:36   NULL:061   NULL:1400000US36061005600  
##  NULL:007400   NULL:36   NULL:061   NULL:1400000US36061007400  
##  NULL:007700   NULL:36   NULL:061   NULL:1400000US36061007700  
##  NULL:008200   NULL:36   NULL:061   NULL:1400000US36061008200  
##  NULL:010200   NULL:36   NULL:061   NULL:1400000US36061010200  
##  NULL:011500   NULL:36   NULL:061   NULL:1400000US36061011500  
##  NULL:012800   NULL:36   NULL:061   NULL:1400000US36061012800  
##  NULL:014401   NULL:36   NULL:061   NULL:1400000US36061014401  
##  NULL:015002   NULL:36   NULL:061   NULL:1400000US36061015002  
##  NULL:015802   NULL:36   NULL:061   NULL:1400000US36061015802  
##  NULL:017300   NULL:36   NULL:061   NULL:1400000US36061017300  
##  NULL:018500   NULL:36   NULL:061   NULL:1400000US36061018500  
##  NULL:018600   NULL:36   NULL:061   NULL:1400000US36061018600  
##  NULL:022400   NULL:36   NULL:061   NULL:1400000US36061022400  
##  NULL:023600   NULL:36   NULL:061   NULL:1400000US36061023600  
##  NULL:024900   NULL:36   NULL:061   NULL:1400000US36061024900  
##  NULL:025300   NULL:36   NULL:061   NULL:1400000US36061025300  
##  NULL:026700   NULL:36   NULL:061   NULL:1400000US36061026700  
##  NULL:031704   NULL:36   NULL:061   NULL:1400000US36061031704  
##  NULL:001300   NULL:36   NULL:061   NULL:1400000US36061001300  
##  NULL:002000   NULL:36   NULL:061   NULL:1400000US36061002000  
##  NULL:003100   NULL:36   NULL:061   NULL:1400000US36061003100  
##  NULL:003602   NULL:36   NULL:061   NULL:1400000US36061003602  
##  NULL:004300   NULL:36   NULL:061   NULL:1400000US36061004300  
##  NULL:004700   NULL:36   NULL:061   NULL:1400000US36061004700  
##  NULL:006300   NULL:36   NULL:061   NULL:1400000US36061006300  
##  NULL:008700   NULL:36   NULL:061   NULL:1400000US36061008700  
##  NULL:009500   NULL:36   NULL:061   NULL:1400000US36061009500  
##  NULL:009800   NULL:36   NULL:061   NULL:1400000US36061009800  
##  NULL:010602   NULL:36   NULL:061   NULL:1400000US36061010602  
##  NULL:011100   NULL:36   NULL:061   NULL:1400000US36061011100  
##  NULL:012700   NULL:36   NULL:061   NULL:1400000US36061012700  
##  NULL:013000   NULL:36   NULL:061   NULL:1400000US36061013000  
##  NULL:013900   NULL:36   NULL:061   NULL:1400000US36061013900  
##  NULL:014500   NULL:36   NULL:061   NULL:1400000US36061014500  
##  NULL:014801   NULL:36   NULL:061   NULL:1400000US36061014801  
##  NULL:016400   NULL:36   NULL:061   NULL:1400000US36061016400  
##  NULL:016900   NULL:36   NULL:061   NULL:1400000US36061016900  
##  NULL:017401   NULL:36   NULL:061   NULL:1400000US36061017401  
##  NULL:018100   NULL:36   NULL:061   NULL:1400000US36061018100  
##  NULL:019200   NULL:36   NULL:061   NULL:1400000US36061019200  
##  NULL:019800   NULL:36   NULL:061   NULL:1400000US36061019800  
##  NULL:020300   NULL:36   NULL:061   NULL:1400000US36061020300  
##  NULL:021200   NULL:36   NULL:061   NULL:1400000US36061021200  
##  NULL:022200   NULL:36   NULL:061   NULL:1400000US36061022200  
##  NULL:022600   NULL:36   NULL:061   NULL:1400000US36061022600  
##  NULL:023501   NULL:36   NULL:061   NULL:1400000US36061023501  
##  NULL:024100   NULL:36   NULL:061   NULL:1400000US36061024100  
##  NULL:029100   NULL:36   NULL:061   NULL:1400000US36061029100  
##  NULL:030900   NULL:36   NULL:061   NULL:1400000US36061030900  
##  NULL:000201   NULL:36   NULL:061   NULL:1400000US36061000201  
##  NULL:003002   NULL:36   NULL:061   NULL:1400000US36061003002  
##  NULL:006700   NULL:36   NULL:061   NULL:1400000US36061006700  
##  NULL:008300   NULL:36   NULL:061   NULL:1400000US36061008300  
##  NULL:011402   NULL:36   NULL:061   NULL:1400000US36061011402  
##  NULL:016500   NULL:36   NULL:061   NULL:1400000US36061016500  
##  NULL:019000   NULL:36   NULL:061   NULL:1400000US36061019000  
##  NULL:021500   NULL:36   NULL:061   NULL:1400000US36061021500  
##  NULL:023300   NULL:36   NULL:061   NULL:1400000US36061023300  
##  NULL:026500   NULL:36   NULL:061   NULL:1400000US36061026500  
##  NULL:006500   NULL:36   NULL:061   NULL:1400000US36061006500  
##  NULL:020500   NULL:36   NULL:061   NULL:1400000US36061020500  
##  NULL:024700   NULL:36   NULL:061   NULL:1400000US36061024700  
##  NULL:001001   NULL:36   NULL:061   NULL:1400000US36061001001  
##  NULL:003001   NULL:36   NULL:061   NULL:1400000US36061003001  
##  NULL:006100   NULL:36   NULL:061   NULL:1400000US36061006100  
##  NULL:010100   NULL:36   NULL:061   NULL:1400000US36061010100  
##  NULL:014000   NULL:36   NULL:061   NULL:1400000US36061014000  
##  NULL:020701   NULL:36   NULL:061   NULL:1400000US36061020701  
##  NULL:000700   NULL:36   NULL:061   NULL:1400000US36061000700  
##  NULL:000900   NULL:36   NULL:061   NULL:1400000US36061000900  
##  NULL:001502   NULL:36   NULL:061   NULL:1400000US36061001502  
##  NULL:002400   NULL:36   NULL:061   NULL:1400000US36061002400  
##  NULL:002800   NULL:36   NULL:061   NULL:1400000US36061002800  
##  NULL:003601   NULL:36   NULL:061   NULL:1400000US36061003601  
##  NULL:003700   NULL:36   NULL:061   NULL:1400000US36061003700  
##  NULL:004800   NULL:36   NULL:061   NULL:1400000US36061004800  
##  NULL:005200   NULL:36   NULL:061   NULL:1400000US36061005200  
##  NULL:005700   NULL:36   NULL:061   NULL:1400000US36061005700  
##  NULL:006400   NULL:36   NULL:061   NULL:1400000US36061006400  
##  NULL:006600   NULL:36   NULL:061   NULL:1400000US36061006600  
##  NULL:006900   NULL:36   NULL:061   NULL:1400000US36061006900  
##  NULL:007000   NULL:36   NULL:061   NULL:1400000US36061007000  
##  NULL:007100   NULL:36   NULL:061   NULL:1400000US36061007100  
##  NULL:007500   NULL:36   NULL:061   NULL:1400000US36061007500  
##  NULL:007600   NULL:36   NULL:061   NULL:1400000US36061007600  
##  NULL:008000   NULL:36   NULL:061   NULL:1400000US36061008000  
##  NULL:008100   NULL:36   NULL:061   NULL:1400000US36061008100  
##  NULL:008601   NULL:36   NULL:061   NULL:1400000US36061008601  
##  NULL:008900   NULL:36   NULL:061   NULL:1400000US36061008900  
##  NULL:009600   NULL:36   NULL:061   NULL:1400000US36061009600  
##  NULL:009900   NULL:36   NULL:061   NULL:1400000US36061009900  
##  NULL:011700   NULL:36   NULL:061   NULL:1400000US36061011700  
##  NULL:012600   NULL:36   NULL:061   NULL:1400000US36061012600  
##  NULL:013100   NULL:36   NULL:061   NULL:1400000US36061013100  
##  NULL:013600   NULL:36   NULL:061   NULL:1400000US36061013600  
##  NULL:014200   NULL:36   NULL:061   NULL:1400000US36061014200  
##  NULL:014700   NULL:36   NULL:061   NULL:1400000US36061014700  
##  NULL:014900   NULL:36   NULL:061   NULL:1400000US36061014900  
##  NULL:015700   NULL:36   NULL:061   NULL:1400000US36061015700  
##  NULL:016002   NULL:36   NULL:061   NULL:1400000US36061016002  
##  NULL:016300   NULL:36   NULL:061   NULL:1400000US36061016300  
##  NULL:017000   NULL:36   NULL:061   NULL:1400000US36061017000  
##  NULL:017402   NULL:36   NULL:061   NULL:1400000US36061017402  
##  NULL:019300   NULL:36   NULL:061   NULL:1400000US36061019300  
##  NULL:019500   NULL:36   NULL:061   NULL:1400000US36061019500  
##  NULL:019900   NULL:36   NULL:061   NULL:1400000US36061019900  
##  NULL:020800   NULL:36   NULL:061   NULL:1400000US36061020800  
##  NULL:011300   NULL:36   NULL:061   NULL:1400000US36061011300  
##  NULL:021303   NULL:36   NULL:061   NULL:1400000US36061021303  
##  NULL:022301   NULL:36   NULL:061   NULL:1400000US36061022301  
##  NULL:022700   NULL:36   NULL:061   NULL:1400000US36061022700  
##  NULL:023200   NULL:36   NULL:061   NULL:1400000US36061023200  
##  NULL:023801   NULL:36   NULL:061   NULL:1400000US36061023801  
##  NULL:024200   NULL:36   NULL:061   NULL:1400000US36061024200  
##  NULL:024301   NULL:36   NULL:061   NULL:1400000US36061024301  
##  NULL:024500   NULL:36   NULL:061   NULL:1400000US36061024500  
##  NULL:025100   NULL:36   NULL:061   NULL:1400000US36061025100  
##  NULL:025900   NULL:36   NULL:061   NULL:1400000US36061025900  
##  NULL:026100   NULL:36   NULL:061   NULL:1400000US36061026100  
##  NULL:026900   NULL:36   NULL:061   NULL:1400000US36061026900  
##  NULL:027100   NULL:36   NULL:061   NULL:1400000US36061027100  
##  NULL:027300   NULL:36   NULL:061   NULL:1400000US36061027300  
##  NULL:028500   NULL:36   NULL:061   NULL:1400000US36061028500  
##  NULL:029300   NULL:36   NULL:061   NULL:1400000US36061029300  
##  NULL:031900   NULL:36   NULL:061   NULL:1400000US36061031900  
##  NULL:000100   NULL:36   NULL:061   NULL:1400000US36061000100  
##  NULL:000600   NULL:36   NULL:061   NULL:1400000US36061000600  
##  NULL:000500   NULL:36   NULL:061   NULL:1400000US36061000500  
##  NULL:000800   NULL:36   NULL:061   NULL:1400000US36061000800  
##  NULL:001002   NULL:36   NULL:061   NULL:1400000US36061001002  
##  NULL:001402   NULL:36   NULL:061   NULL:1400000US36061001402  
##  NULL:001600   NULL:36   NULL:061   NULL:1400000US36061001600  
##  NULL:002202   NULL:36   NULL:061   NULL:1400000US36061002202  
##  NULL:002500   NULL:36   NULL:061   NULL:1400000US36061002500  
##  NULL:002700   NULL:36   NULL:061   NULL:1400000US36061002700  
##  NULL:002900   NULL:36   NULL:061   NULL:1400000US36061002900  
##  NULL:003300   NULL:36   NULL:061   NULL:1400000US36061003300  
##  NULL:003400   NULL:36   NULL:061   NULL:1400000US36061003400  
##  NULL:003800   NULL:36   NULL:061   NULL:1400000US36061003800  
##  NULL:004100   NULL:36   NULL:061   NULL:1400000US36061004100  
##  NULL:004400   NULL:36   NULL:061   NULL:1400000US36061004400  
##  NULL:005400   NULL:36   NULL:061   NULL:1400000US36061005400  
##  NULL:005502   NULL:36   NULL:061   NULL:1400000US36061005502  
##  NULL:005800   NULL:36   NULL:061   NULL:1400000US36061005800  
##  NULL:006000   NULL:36   NULL:061   NULL:1400000US36061006000  
##  NULL:008400   NULL:36   NULL:061   NULL:1400000US36061008400  
##  NULL:008602   NULL:36   NULL:061   NULL:1400000US36061008602  
##  NULL:008800   NULL:36   NULL:061   NULL:1400000US36061008800  
##  NULL:009300   NULL:36   NULL:061   NULL:1400000US36061009300  
##  NULL:009700   NULL:36   NULL:061   NULL:1400000US36061009700  
##  NULL:010000   NULL:36   NULL:061   NULL:1400000US36061010000  
##  NULL:010300   NULL:36   NULL:061   NULL:1400000US36061010300  
##  NULL:010601   NULL:36   NULL:061   NULL:1400000US36061010601  
##  NULL:011401   NULL:36   NULL:061   NULL:1400000US36061011401  
##  NULL:011600   NULL:36   NULL:061   NULL:1400000US36061011600  
##  NULL:011900   NULL:36   NULL:061   NULL:1400000US36061011900  
##  NULL:012400   NULL:36   NULL:061   NULL:1400000US36061012400  
##  NULL:012900   NULL:36   NULL:061   NULL:1400000US36061012900  
##  NULL:023700   NULL:36   NULL:061   NULL:1400000US36061023700  
##  NULL:023900   NULL:36   NULL:061   NULL:1400000US36061023900  
##  NULL:013200   NULL:36   NULL:061   NULL:1400000US36061013200  
##  NULL:028100   NULL:36   NULL:061   NULL:1400000US36061028100  
##  NULL:028300   NULL:36   NULL:061   NULL:1400000US36061028300  
##  NULL:029500   NULL:36   NULL:061   NULL:1400000US36061029500  
##  NULL:030300   NULL:36   NULL:061   NULL:1400000US36061030300  
##  NULL:031703   NULL:36   NULL:061   NULL:1400000US36061031703  
##  NULL:013500   NULL:36   NULL:061   NULL:1400000US36061013500  
##  NULL:013700   NULL:36   NULL:061   NULL:1400000US36061013700  
##  NULL:014300   NULL:36   NULL:061   NULL:1400000US36061014300  
##  NULL:014402   NULL:36   NULL:061   NULL:1400000US36061014402  
##  NULL:014602   NULL:36   NULL:061   NULL:1400000US36061014602  
##  NULL:015001   NULL:36   NULL:061   NULL:1400000US36061015001  
##  NULL:015100   NULL:36   NULL:061   NULL:1400000US36061015100  
##  NULL:015200   NULL:36   NULL:061   NULL:1400000US36061015200  
##  NULL:015601   NULL:36   NULL:061   NULL:1400000US36061015601  
##  NULL:015801   NULL:36   NULL:061   NULL:1400000US36061015801  
##  NULL:015900   NULL:36   NULL:061   NULL:1400000US36061015900  
##  NULL:016100   NULL:36   NULL:061   NULL:1400000US36061016100  
##  NULL:016600   NULL:36   NULL:061   NULL:1400000US36061016600  
##  NULL:017100   NULL:36   NULL:061   NULL:1400000US36061017100  
##  NULL:017200   NULL:36   NULL:061   NULL:1400000US36061017200  
##  NULL:017900   NULL:36   NULL:061   NULL:1400000US36061017900  
##  NULL:018200   NULL:36   NULL:061   NULL:1400000US36061018200  
##  NULL:018400   NULL:36   NULL:061   NULL:1400000US36061018400  
##  NULL:018900   NULL:36   NULL:061   NULL:1400000US36061018900  
##  NULL:019100   NULL:36   NULL:061   NULL:1400000US36061019100  
##  NULL:019400   NULL:36   NULL:061   NULL:1400000US36061019400  
##  NULL:019600   NULL:36   NULL:061   NULL:1400000US36061019600  
##  NULL:020000   NULL:36   NULL:061   NULL:1400000US36061020000  
##  NULL:020101   NULL:36   NULL:061   NULL:1400000US36061020101  
##  NULL:020600   NULL:36   NULL:061   NULL:1400000US36061020600  
##  NULL:020901   NULL:36   NULL:061   NULL:1400000US36061020901  
##  NULL:021100   NULL:36   NULL:061   NULL:1400000US36061021100  
##  NULL:021400   NULL:36   NULL:061   NULL:1400000US36061021400  
##  NULL:021703   NULL:36   NULL:061   NULL:1400000US36061021703  
##  NULL:022500   NULL:36   NULL:061   NULL:1400000US36061022500  
##  NULL:022800   NULL:36   NULL:061   NULL:1400000US36061022800  
##  NULL:023000   NULL:36   NULL:061   NULL:1400000US36061023000  
##  NULL:023502   NULL:36   NULL:061   NULL:1400000US36061023502  
##  NULL:000202   NULL:36   NULL:061   NULL:1400000US36061000202  
##  NULL:025500   NULL:36   NULL:061   NULL:1400000US36061025500  
##  NULL:027500   NULL:36   NULL:061   NULL:1400000US36061027500  
##  NULL:029700   NULL:36   NULL:061   NULL:1400000US36061029700  
##  NULL:001800   NULL:36   NULL:061   NULL:1400000US36061001800  
##  NULL:004200   NULL:36   NULL:061   NULL:1400000US36061004200  
##  NULL:005900   NULL:36   NULL:061   NULL:1400000US36061005900  
##  NULL:007200   NULL:36   NULL:061   NULL:1400000US36061007200  
##  NULL:007800   NULL:36   NULL:061   NULL:1400000US36061007800  
##  NULL:009400   NULL:36   NULL:061   NULL:1400000US36061009400  
##  NULL:011000   NULL:36   NULL:061   NULL:1400000US36061011000  
##  NULL:012000   NULL:36   NULL:061   NULL:1400000US36061012000  
##  NULL:013300   NULL:36   NULL:061   NULL:1400000US36061013300  
##  NULL:014601   NULL:36   NULL:061   NULL:1400000US36061014601  
##  NULL:017800   NULL:36   NULL:061   NULL:1400000US36061017800  
##  NULL:020102   NULL:36   NULL:061   NULL:1400000US36061020102  
##  NULL:021600   NULL:36   NULL:061   NULL:1400000US36061021600  
##  NULL:022302   NULL:36   NULL:061   NULL:1400000US36061022302  
##  NULL:022900   NULL:36   NULL:061   NULL:1400000US36061022900  
##  NULL:024000   NULL:36   NULL:061   NULL:1400000US36061024000  
##  NULL:024302   NULL:36   NULL:061   NULL:1400000US36061024302  
##  NULL:010400   NULL:36   NULL:061   NULL:1400000US36061010400  
##  NULL:025700   NULL:36   NULL:061   NULL:1400000US36061025700  
##  NULL:016001   NULL:36   NULL:061   NULL:1400000US36061016001  
##  NULL:004500   NULL:36   NULL:061   NULL:1400000US36061004500  
##  NULL:001200   NULL:36   NULL:061   NULL:1400000US36061001200  
##  NULL:006200   NULL:36   NULL:061   NULL:1400000US36061006200  
##  NULL:007300   NULL:36   NULL:061   NULL:1400000US36061007300  
##  NULL:007900   NULL:36   NULL:061   NULL:1400000US36061007900  
##  NULL:010800   NULL:36   NULL:061   NULL:1400000US36061010800  
##  NULL:012200   NULL:36   NULL:061   NULL:1400000US36061012200  
##  NULL:013800   NULL:36   NULL:061   NULL:1400000US36061013800  
##  NULL:016800   NULL:36   NULL:061   NULL:1400000US36061016800  
##  NULL:018300   NULL:36   NULL:061   NULL:1400000US36061018300  
##  NULL:022000   NULL:36   NULL:061   NULL:1400000US36061022000  
##  NULL:023400   NULL:36   NULL:061   NULL:1400000US36061023400  
##  NULL:026300   NULL:36   NULL:061   NULL:1400000US36061026300  
##  NULL:027700   NULL:36   NULL:061   NULL:1400000US36061027700  
##  NULL:031100   NULL:36   NULL:061   NULL:1400000US36061031100  
##  NULL:001501   NULL:36   NULL:061   NULL:1400000US36061001501  
##  NULL:009200   NULL:36   NULL:061   NULL:1400000US36061009200  
##  NULL:016200   NULL:36   NULL:061   NULL:1400000US36061016200  
##  NULL:021900   NULL:36   NULL:061   NULL:1400000US36061021900  
##  NULL:028700   NULL:36   NULL:061   NULL:1400000US36061028700  
##  NULL:002100   NULL:36   NULL:061   NULL:1400000US36061002100  
##  NULL:003900   NULL:36   NULL:061   NULL:1400000US36061003900  
##  NULL:005000   NULL:36   NULL:061   NULL:1400000US36061005000  
##  NULL:009000   NULL:36   NULL:061   NULL:1400000US36061009000  
##  NULL:011201   NULL:36   NULL:061   NULL:1400000US36061011201  
##  NULL:012100   NULL:36   NULL:061   NULL:1400000US36061012100  
##  NULL:013400   NULL:36   NULL:061   NULL:1400000US36061013400  
##  NULL:014802   NULL:36   NULL:061   NULL:1400000US36061014802  
##  NULL:015602   NULL:36   NULL:061   NULL:1400000US36061015602  
##  NULL:016700   NULL:36   NULL:061   NULL:1400000US36061016700  
##  NULL:018700   NULL:36   NULL:061   NULL:1400000US36061018700  
##  NULL:019701   NULL:36   NULL:061   NULL:1400000US36061019701  
##  NULL:022102   NULL:36   NULL:061   NULL:1400000US36061022102  
##  NULL:023802   NULL:36   NULL:061   NULL:1400000US36061023802  
##  NULL:015300   NULL:36   NULL:061   NULL:1400000US36061015300  
##  NULL:029900   NULL:36   NULL:061   NULL:1400000US36061029900  
##  NULL:011203   NULL:36   NULL:061   NULL:1400000US36061011203  
##  NULL:017500   NULL:36   NULL:061   NULL:1400000US36061017500  
##  NULL:018800   NULL:36   NULL:061   NULL:1400000US36061018800  
##  NULL:018000   NULL:36   NULL:061   NULL:1400000US36061018000  
##  NULL:021800   NULL:36   NULL:061   NULL:1400000US36061021800  
##  NULL:017700   NULL:36   NULL:061   NULL:1400000US36061017700  
##  NULL:004900   NULL:36   NULL:061   NULL:1400000US36061004900  
##  NULL:010900   NULL:36   NULL:061   NULL:1400000US36061010900  
##  NULL:015500   NULL:36   NULL:061   NULL:1400000US36061015500  
##  NULL:008603   NULL:36   NULL:061   NULL:1400000US36061008603  
##  NULL:023100   NULL:36   NULL:061   NULL:1400000US36061023100  
##  NULL:011202   NULL:36   NULL:061   NULL:1400000US36061011202  
##  NULL:012500   NULL:36   NULL:061   NULL:1400000US36061012500  
##  NULL:006800   NULL:36   NULL:061   NULL:1400000US36061006800  
##  NULL:019702   NULL:36   NULL:061   NULL:1400000US36061019702  
##  NULL:030700   NULL:36   NULL:061   NULL:1400000US36061030700  
##  NULL:002602   NULL:36   NULL:061   NULL:1400000US36061002602  
##  NULL:002601   NULL:36   NULL:061   NULL:1400000US36061002601  
##  NULL:011800   NULL:36   NULL:061   NULL:1400000US36061011800  
##  NULL:015400   NULL:36   NULL:061   NULL:1400000US36061015400  
##  NULL:009100   NULL:36   NULL:061   NULL:1400000US36061009100  
##  NULL:021000   NULL:36   NULL:061   NULL:1400000US36061021000  
##  NULL:027900   NULL:36   NULL:061   NULL:1400000US36061027900  
##   GEOID               NAME          LSAD     ALAND          AWATER       
##  NULL:36061001401   NULL:14.01    NULL:CT   NULL:93510     NULL:0        
##  NULL:36061002201   NULL:22.01    NULL:CT   NULL:161667    NULL:0        
##  NULL:36061003200   NULL:32       NULL:CT   NULL:217682    NULL:0        
##  NULL:36061004000   NULL:40       NULL:CT   NULL:178340    NULL:0        
##  NULL:36061005501   NULL:55.01    NULL:CT   NULL:124447    NULL:0        
##  NULL:36061005600   NULL:56       NULL:CT   NULL:174425    NULL:0        
##  NULL:36061007400   NULL:74       NULL:CT   NULL:174085    NULL:0        
##  NULL:36061007700   NULL:77       NULL:CT   NULL:178798    NULL:0        
##  NULL:36061008200   NULL:82       NULL:CT   NULL:172401    NULL:0        
##  NULL:36061010200   NULL:102      NULL:CT   NULL:172891    NULL:0        
##  NULL:36061011500   NULL:115      NULL:CT   NULL:175588    NULL:0        
##  NULL:36061012800   NULL:128      NULL:CT   NULL:178790    NULL:0        
##  NULL:36061014401   NULL:144.01   NULL:CT   NULL:74319     NULL:0        
##  NULL:36061015002   NULL:150.02   NULL:CT   NULL:125571    NULL:0        
##  NULL:36061015802   NULL:158.02   NULL:CT   NULL:51510     NULL:0        
##  NULL:36061017300   NULL:173      NULL:CT   NULL:178602    NULL:0        
##  NULL:36061018500   NULL:185      NULL:CT   NULL:130383    NULL:0        
##  NULL:36061018600   NULL:186      NULL:CT   NULL:121738    NULL:0        
##  NULL:36061022400   NULL:224      NULL:CT   NULL:194687    NULL:0        
##  NULL:36061023600   NULL:236      NULL:CT   NULL:263158    NULL:127979   
##  NULL:36061024900   NULL:249      NULL:CT   NULL:60659     NULL:0        
##  NULL:36061025300   NULL:253      NULL:CT   NULL:169349    NULL:0        
##  NULL:36061026700   NULL:267      NULL:CT   NULL:47338     NULL:0        
##  NULL:36061031704   NULL:317.04   NULL:CT   NULL:219944    NULL:473123   
##  NULL:36061001300   NULL:13       NULL:CT   NULL:312732    NULL:0        
##  NULL:36061002000   NULL:20       NULL:CT   NULL:126283    NULL:137883   
##  NULL:36061003100   NULL:31       NULL:CT   NULL:204969    NULL:0        
##  NULL:36061003602   NULL:36.02    NULL:CT   NULL:89011     NULL:0        
##  NULL:36061004300   NULL:43       NULL:CT   NULL:149239    NULL:0        
##  NULL:36061004700   NULL:47       NULL:CT   NULL:165421    NULL:0        
##  NULL:36061006300   NULL:63       NULL:CT   NULL:178187    NULL:0        
##  NULL:36061008700   NULL:87       NULL:CT   NULL:165443    NULL:0        
##  NULL:36061009500   NULL:95       NULL:CT   NULL:171964    NULL:0        
##  NULL:36061009800   NULL:98       NULL:CT   NULL:177068    NULL:0        
##  NULL:36061010602   NULL:106.02   NULL:CT   NULL:125711    NULL:73073    
##  NULL:36061011100   NULL:111      NULL:CT   NULL:175465    NULL:0        
##  NULL:36061012700   NULL:127      NULL:CT   NULL:174026    NULL:0        
##  NULL:36061013000   NULL:130      NULL:CT   NULL:178226    NULL:0        
##  NULL:36061013900   NULL:139      NULL:CT   NULL:181595    NULL:0        
##  NULL:36061014500   NULL:145      NULL:CT   NULL:178321    NULL:0        
##  NULL:36061014801   NULL:148.01   NULL:CT   NULL:51979     NULL:0        
##  NULL:36061016400   NULL:164      NULL:CT   NULL:177390    NULL:0        
##  NULL:36061016900   NULL:169      NULL:CT   NULL:180002    NULL:0        
##  NULL:36061017401   NULL:174.01   NULL:CT   NULL:100812    NULL:0        
##  NULL:36061018100   NULL:181      NULL:CT   NULL:138455    NULL:0        
##  NULL:36061019200   NULL:192      NULL:CT   NULL:189854    NULL:175645   
##  NULL:36061019800   NULL:198      NULL:CT   NULL:225452    NULL:0        
##  NULL:36061020300   NULL:203      NULL:CT   NULL:178223    NULL:0        
##  NULL:36061021200   NULL:212      NULL:CT   NULL:176416    NULL:0        
##  NULL:36061022200   NULL:222      NULL:CT   NULL:181422    NULL:0        
##  NULL:36061022600   NULL:226      NULL:CT   NULL:152979    NULL:0        
##  NULL:36061023501   NULL:235.01   NULL:CT   NULL:179045    NULL:0        
##  NULL:36061024100   NULL:241      NULL:CT   NULL:178781    NULL:207330   
##  NULL:36061029100   NULL:291      NULL:CT   NULL:187479    NULL:0        
##  NULL:36061030900   NULL:309      NULL:CT   NULL:302752    NULL:0        
##  NULL:36061000201   NULL:2.01     NULL:CT   NULL:90233     NULL:75976    
##  NULL:36061003002   NULL:30.02    NULL:CT   NULL:78525     NULL:0        
##  NULL:36061006700   NULL:67       NULL:CT   NULL:168478    NULL:0        
##  NULL:36061008300   NULL:83       NULL:CT   NULL:174158    NULL:0        
##  NULL:36061011402   NULL:114.02   NULL:CT   NULL:98799     NULL:0        
##  NULL:36061016500   NULL:165      NULL:CT   NULL:204317    NULL:0        
##  NULL:36061019000   NULL:190      NULL:CT   NULL:103802    NULL:0        
##  NULL:36061021500   NULL:215      NULL:CT   NULL:69710     NULL:0        
##  NULL:36061023300   NULL:233      NULL:CT   NULL:232212    NULL:285722   
##  NULL:36061026500   NULL:265      NULL:CT   NULL:195037    NULL:0        
##  NULL:36061006500   NULL:65       NULL:CT   NULL:201131    NULL:0        
##  NULL:36061020500   NULL:205      NULL:CT   NULL:281121    NULL:374540   
##  NULL:36061024700   NULL:247      NULL:CT   NULL:256348    NULL:400307   
##  NULL:36061001001   NULL:10.01    NULL:CT   NULL:80145     NULL:244297   
##  NULL:36061003001   NULL:30.01    NULL:CT   NULL:124153    NULL:0        
##  NULL:36061006100   NULL:61       NULL:CT   NULL:142017    NULL:0        
##  NULL:36061010100   NULL:101      NULL:CT   NULL:175518    NULL:0        
##  NULL:36061014000   NULL:140      NULL:CT   NULL:179108    NULL:0        
##  NULL:36061020701   NULL:207.01   NULL:CT   NULL:47273     NULL:0        
##  NULL:36061000700   NULL:7        NULL:CT   NULL:256720    NULL:242749   
##  NULL:36061000900   NULL:9        NULL:CT   NULL:315105    NULL:460146   
##  NULL:36061001502   NULL:15.02    NULL:CT   NULL:207277    NULL:146802   
##  NULL:36061002400   NULL:24       NULL:CT   NULL:182345    NULL:470253   
##  NULL:36061002800   NULL:28       NULL:CT   NULL:183616    NULL:0        
##  NULL:36061003601   NULL:36.01    NULL:CT   NULL:99812     NULL:0        
##  NULL:36061003700   NULL:37       NULL:CT   NULL:383376    NULL:322356   
##  NULL:36061004800   NULL:48       NULL:CT   NULL:175756    NULL:0        
##  NULL:36061005200   NULL:52       NULL:CT   NULL:168892    NULL:0        
##  NULL:36061005700   NULL:57       NULL:CT   NULL:108054    NULL:0        
##  NULL:36061006400   NULL:64       NULL:CT   NULL:179196    NULL:0        
##  NULL:36061006600   NULL:66       NULL:CT   NULL:174704    NULL:0        
##  NULL:36061006900   NULL:69       NULL:CT   NULL:220486    NULL:275547   
##  NULL:36061007000   NULL:70       NULL:CT   NULL:177329    NULL:0        
##  NULL:36061007100   NULL:71       NULL:CT   NULL:164996    NULL:0        
##  NULL:36061007500   NULL:75       NULL:CT   NULL:165732    NULL:283835   
##  NULL:36061007600   NULL:76       NULL:CT   NULL:174274    NULL:0        
##  NULL:36061008000   NULL:80       NULL:CT   NULL:172626    NULL:0        
##  NULL:36061008100   NULL:81       NULL:CT   NULL:174287    NULL:0        
##  NULL:36061008601   NULL:86.01    NULL:CT   NULL:125340    NULL:499842   
##  NULL:36061008900   NULL:89       NULL:CT   NULL:165578    NULL:0        
##  NULL:36061009600   NULL:96       NULL:CT   NULL:175329    NULL:0        
##  NULL:36061009900   NULL:99       NULL:CT   NULL:1002374   NULL:1130336  
##  NULL:36061011700   NULL:117      NULL:CT   NULL:187347    NULL:176830   
##  NULL:36061012600   NULL:126      NULL:CT   NULL:183653    NULL:0        
##  NULL:36061013100   NULL:131      NULL:CT   NULL:174140    NULL:0        
##  NULL:36061013600   NULL:136      NULL:CT   NULL:219496    NULL:111000   
##  NULL:36061014200   NULL:142      NULL:CT   NULL:178987    NULL:0        
##  NULL:36061014700   NULL:147      NULL:CT   NULL:65414     NULL:0        
##  NULL:36061014900   NULL:149      NULL:CT   NULL:174047    NULL:0        
##  NULL:36061015700   NULL:157      NULL:CT   NULL:182396    NULL:0        
##  NULL:36061016002   NULL:160.02   NULL:CT   NULL:51424     NULL:0        
##  NULL:36061016300   NULL:163      NULL:CT   NULL:263616    NULL:352996   
##  NULL:36061017000   NULL:170      NULL:CT   NULL:182916    NULL:0        
##  NULL:36061017402   NULL:174.02   NULL:CT   NULL:50730     NULL:0        
##  NULL:36061019300   NULL:193      NULL:CT   NULL:175716    NULL:0        
##  NULL:36061019500   NULL:195      NULL:CT   NULL:218436    NULL:166301   
##  NULL:36061019900   NULL:199      NULL:CT   NULL:226590    NULL:192056   
##  NULL:36061020800   NULL:208      NULL:CT   NULL:148566    NULL:0        
##  NULL:36061011300   NULL:113      NULL:CT   NULL:175653    NULL:0        
##  NULL:36061021303   NULL:213.03   NULL:CT   NULL:153292    NULL:0        
##  NULL:36061022301   NULL:223.01   NULL:CT   NULL:186804    NULL:131253   
##  NULL:36061022700   NULL:227      NULL:CT   NULL:150703    NULL:0        
##  NULL:36061023200   NULL:232      NULL:CT   NULL:180486    NULL:0        
##  NULL:36061023801   NULL:238.01   NULL:CT   NULL:352467    NULL:518659   
##  NULL:36061024200   NULL:242      NULL:CT   NULL:347358    NULL:135920   
##  NULL:36061024301   NULL:243.01   NULL:CT   NULL:105194    NULL:0        
##  NULL:36061024500   NULL:245      NULL:CT   NULL:212070    NULL:0        
##  NULL:36061025100   NULL:251      NULL:CT   NULL:159422    NULL:0        
##  NULL:36061025900   NULL:259      NULL:CT   NULL:75469     NULL:0        
##  NULL:36061026100   NULL:261      NULL:CT   NULL:156222    NULL:0        
##  NULL:36061026900   NULL:269      NULL:CT   NULL:175570    NULL:0        
##  NULL:36061027100   NULL:271      NULL:CT   NULL:192909    NULL:0        
##  NULL:36061027300   NULL:273      NULL:CT   NULL:176946    NULL:0        
##  NULL:36061028500   NULL:285      NULL:CT   NULL:121942    NULL:0        
##  NULL:36061029300   NULL:293      NULL:CT   NULL:157076    NULL:0        
##  NULL:36061031900   NULL:319      NULL:CT   NULL:103141    NULL:314424   
##  NULL:36061000100   NULL:1        NULL:CT   NULL:78638     NULL:0        
##  NULL:36061000600   NULL:6        NULL:CT   NULL:240406    NULL:176018   
##  NULL:36061000500   NULL:5        NULL:CT   NULL:843918    NULL:3393360  
##  NULL:36061000800   NULL:8        NULL:CT   NULL:220708    NULL:167906   
##  NULL:36061001002   NULL:10.02    NULL:CT   NULL:199683    NULL:245138   
##  NULL:36061001402   NULL:14.02    NULL:CT   NULL:113920    NULL:0        
##  NULL:36061001600   NULL:16       NULL:CT   NULL:207377    NULL:0        
##  NULL:36061002202   NULL:22.02    NULL:CT   NULL:56112     NULL:0        
##  NULL:36061002500   NULL:25       NULL:CT   NULL:131517    NULL:94115    
##  NULL:36061002700   NULL:27       NULL:CT   NULL:59282     NULL:0        
##  NULL:36061002900   NULL:29       NULL:CT   NULL:273357    NULL:0        
##  NULL:36061003300   NULL:33       NULL:CT   NULL:341677    NULL:0        
##  NULL:36061003400   NULL:34       NULL:CT   NULL:160257    NULL:0        
##  NULL:36061003800   NULL:38       NULL:CT   NULL:202371    NULL:0        
##  NULL:36061004100   NULL:41       NULL:CT   NULL:191275    NULL:0        
##  NULL:36061004400   NULL:44       NULL:CT   NULL:410012    NULL:465240   
##  NULL:36061005400   NULL:54       NULL:CT   NULL:167959    NULL:0        
##  NULL:36061005502   NULL:55.02    NULL:CT   NULL:115486    NULL:0        
##  NULL:36061005800   NULL:58       NULL:CT   NULL:174669    NULL:0        
##  NULL:36061006000   NULL:60       NULL:CT   NULL:118942    NULL:300815   
##  NULL:36061008400   NULL:84       NULL:CT   NULL:173067    NULL:0        
##  NULL:36061008602   NULL:86.02    NULL:CT   NULL:106753    NULL:317001   
##  NULL:36061008800   NULL:88       NULL:CT   NULL:179256    NULL:0        
##  NULL:36061009300   NULL:93       NULL:CT   NULL:179101    NULL:0        
##  NULL:36061009700   NULL:97       NULL:CT   NULL:172931    NULL:0        
##  NULL:36061010000   NULL:100      NULL:CT   NULL:172894    NULL:0        
##  NULL:36061010300   NULL:103      NULL:CT   NULL:174470    NULL:0        
##  NULL:36061010601   NULL:106.01   NULL:CT   NULL:121208    NULL:130046   
##  NULL:36061011401   NULL:114.01   NULL:CT   NULL:98508     NULL:0        
##  NULL:36061011600   NULL:116      NULL:CT   NULL:195582    NULL:132923   
##  NULL:36061011900   NULL:119      NULL:CT   NULL:177591    NULL:0        
##  NULL:36061012400   NULL:124      NULL:CT   NULL:171580    NULL:75103    
##  NULL:36061012900   NULL:129      NULL:CT   NULL:404224    NULL:407508   
##  NULL:36061023700   NULL:237      NULL:CT   NULL:271911    NULL:273621   
##  NULL:36061023900   NULL:239      NULL:CT   NULL:63850     NULL:0        
##  NULL:36061013200   NULL:132      NULL:CT   NULL:200886    NULL:92937    
##  NULL:36061028100   NULL:281      NULL:CT   NULL:94227     NULL:0        
##  NULL:36061028300   NULL:283      NULL:CT   NULL:151093    NULL:0        
##  NULL:36061029500   NULL:295      NULL:CT   NULL:135565    NULL:0        
##  NULL:36061030300   NULL:303      NULL:CT   NULL:156650    NULL:0        
##  NULL:36061031703   NULL:317.03   NULL:CT   NULL:258090    NULL:467423   
##  NULL:36061013500   NULL:135      NULL:CT   NULL:432329    NULL:401309   
##  NULL:36061013700   NULL:137      NULL:CT   NULL:225671    NULL:0        
##  NULL:36061014300   NULL:143      NULL:CT   NULL:2991654   NULL:567106   
##  NULL:36061014402   NULL:144.02   NULL:CT   NULL:176863    NULL:0        
##  NULL:36061014602   NULL:146.02   NULL:CT   NULL:109193    NULL:0        
##  NULL:36061015001   NULL:150.01   NULL:CT   NULL:51643     NULL:0        
##  NULL:36061015100   NULL:151      NULL:CT   NULL:333044    NULL:306023   
##  NULL:36061015200   NULL:152      NULL:CT   NULL:189341    NULL:529192   
##  NULL:36061015601   NULL:156.01   NULL:CT   NULL:73566     NULL:0        
##  NULL:36061015801   NULL:158.01   NULL:CT   NULL:125754    NULL:0        
##  NULL:36061015900   NULL:159      NULL:CT   NULL:175127    NULL:110770   
##  NULL:36061016100   NULL:161      NULL:CT   NULL:155741    NULL:0        
##  NULL:36061016600   NULL:166      NULL:CT   NULL:174218    NULL:0        
##  NULL:36061017100   NULL:171      NULL:CT   NULL:217835    NULL:194050   
##  NULL:36061017200   NULL:172      NULL:CT   NULL:177939    NULL:0        
##  NULL:36061017900   NULL:179      NULL:CT   NULL:213916    NULL:190568   
##  NULL:36061018200   NULL:182      NULL:CT   NULL:176899    NULL:0        
##  NULL:36061018400   NULL:184      NULL:CT   NULL:176580    NULL:0        
##  NULL:36061018900   NULL:189      NULL:CT   NULL:265399    NULL:0        
##  NULL:36061019100   NULL:191      NULL:CT   NULL:223476    NULL:175654   
##  NULL:36061019400   NULL:194      NULL:CT   NULL:175052    NULL:0        
##  NULL:36061019600   NULL:196      NULL:CT   NULL:177186    NULL:0        
##  NULL:36061020000   NULL:200      NULL:CT   NULL:129287    NULL:0        
##  NULL:36061020101   NULL:201.01   NULL:CT   NULL:55127     NULL:0        
##  NULL:36061020600   NULL:206      NULL:CT   NULL:148748    NULL:0        
##  NULL:36061020901   NULL:209.01   NULL:CT   NULL:115805    NULL:0        
##  NULL:36061021100   NULL:211      NULL:CT   NULL:347999    NULL:307945   
##  NULL:36061021400   NULL:214      NULL:CT   NULL:135562    NULL:0        
##  NULL:36061021703   NULL:217.03   NULL:CT   NULL:313864    NULL:0        
##  NULL:36061022500   NULL:225      NULL:CT   NULL:262466    NULL:136149   
##  NULL:36061022800   NULL:228      NULL:CT   NULL:180634    NULL:0        
##  NULL:36061023000   NULL:230      NULL:CT   NULL:173684    NULL:0        
##  NULL:36061023502   NULL:235.02   NULL:CT   NULL:41338     NULL:0        
##  NULL:36061000202   NULL:2.02     NULL:CT   NULL:310039    NULL:428737   
##  NULL:36061025500   NULL:255      NULL:CT   NULL:441356    NULL:586786   
##  NULL:36061027500   NULL:275      NULL:CT   NULL:279689    NULL:852909   
##  NULL:36061029700   NULL:297      NULL:CT   NULL:948118    NULL:1006331  
##  NULL:36061001800   NULL:18       NULL:CT   NULL:222968    NULL:0        
##  NULL:36061004200   NULL:42       NULL:CT   NULL:85723     NULL:0        
##  NULL:36061005900   NULL:59       NULL:CT   NULL:116172    NULL:0        
##  NULL:36061007200   NULL:72       NULL:CT   NULL:175015    NULL:0        
##  NULL:36061007800   NULL:78       NULL:CT   NULL:177474    NULL:0        
##  NULL:36061009400   NULL:94       NULL:CT   NULL:152754    NULL:0        
##  NULL:36061011000   NULL:110      NULL:CT   NULL:176509    NULL:0        
##  NULL:36061012000   NULL:120      NULL:CT   NULL:173024    NULL:0        
##  NULL:36061013300   NULL:133      NULL:CT   NULL:174356    NULL:0        
##  NULL:36061014601   NULL:146.01   NULL:CT   NULL:74328     NULL:0        
##  NULL:36061017800   NULL:178      NULL:CT   NULL:248851    NULL:209809   
##  NULL:36061020102   NULL:201.02   NULL:CT   NULL:83350     NULL:0        
##  NULL:36061021600   NULL:216      NULL:CT   NULL:173198    NULL:0        
##  NULL:36061022302   NULL:223.02   NULL:CT   NULL:60095     NULL:96898    
##  NULL:36061022900   NULL:229      NULL:CT   NULL:211053    NULL:131446   
##  NULL:36061024000   NULL:240      NULL:CT   NULL:2183933   NULL:793007   
##  NULL:36061024302   NULL:243.02   NULL:CT   NULL:213258    NULL:113258   
##  NULL:36061010400   NULL:104      NULL:CT   NULL:173232    NULL:0        
##  NULL:36061025700   NULL:257      NULL:CT   NULL:144063    NULL:0        
##  NULL:36061016001   NULL:160.01   NULL:CT   NULL:125615    NULL:0        
##  NULL:36061004500   NULL:45       NULL:CT   NULL:104947    NULL:0        
##  NULL:36061001200   NULL:12       NULL:CT   NULL:120597    NULL:0        
##  NULL:36061006200   NULL:62       NULL:CT   NULL:293510    NULL:862281   
##  NULL:36061007300   NULL:73       NULL:CT   NULL:165299    NULL:0        
##  NULL:36061007900   NULL:79       NULL:CT   NULL:285956    NULL:502030   
##  NULL:36061010800   NULL:108      NULL:CT   NULL:182589    NULL:0        
##  NULL:36061012200   NULL:122      NULL:CT   NULL:172616    NULL:0        
##  NULL:36061013800   NULL:138      NULL:CT   NULL:182091    NULL:0        
##  NULL:36061016800   NULL:168      NULL:CT   NULL:199278    NULL:0        
##  NULL:36061018300   NULL:183      NULL:CT   NULL:224312    NULL:187898   
##  NULL:36061022000   NULL:220      NULL:CT   NULL:174955    NULL:0        
##  NULL:36061023400   NULL:234      NULL:CT   NULL:108235    NULL:0        
##  NULL:36061026300   NULL:263      NULL:CT   NULL:157201    NULL:0        
##  NULL:36061027700   NULL:277      NULL:CT   NULL:163262    NULL:0        
##  NULL:36061031100   NULL:311      NULL:CT   NULL:730774    NULL:312736   
##  NULL:36061001501   NULL:15.01    NULL:CT   NULL:257851    NULL:175058   
##  NULL:36061009200   NULL:92       NULL:CT   NULL:197023    NULL:0        
##  NULL:36061016200   NULL:162      NULL:CT   NULL:272018    NULL:832556   
##  NULL:36061021900   NULL:219      NULL:CT   NULL:279408    NULL:191783   
##  NULL:36061028700   NULL:287      NULL:CT   NULL:532366    NULL:1018817  
##  NULL:36061002100   NULL:21       NULL:CT   NULL:236794    NULL:0        
##  NULL:36061003900   NULL:39       NULL:CT   NULL:331072    NULL:447417   
##  NULL:36061005000   NULL:50       NULL:CT   NULL:166467    NULL:0        
##  NULL:36061009000   NULL:90       NULL:CT   NULL:174692    NULL:0        
##  NULL:36061011201   NULL:112.01   NULL:CT   NULL:79536     NULL:0        
##  NULL:36061012100   NULL:121      NULL:CT   NULL:178161    NULL:0        
##  NULL:36061013400   NULL:134      NULL:CT   NULL:181779    NULL:0        
##  NULL:36061014802   NULL:148.02   NULL:CT   NULL:125620    NULL:0        
##  NULL:36061015602   NULL:156.02   NULL:CT   NULL:108755    NULL:0        
##  NULL:36061016700   NULL:167      NULL:CT   NULL:196978    NULL:141457   
##  NULL:36061018700   NULL:187      NULL:CT   NULL:218107    NULL:184467   
##  NULL:36061019701   NULL:197.01   NULL:CT   NULL:235976    NULL:0        
##  NULL:36061022102   NULL:221.02   NULL:CT   NULL:99000     NULL:0        
##  NULL:36061023802   NULL:238.02   NULL:CT   NULL:243417    NULL:313651   
##  NULL:36061015300   NULL:153      NULL:CT   NULL:174116    NULL:0        
##  NULL:36061029900   NULL:299      NULL:CT   NULL:616054    NULL:322917   
##  NULL:36061011203   NULL:112.03   NULL:CT   NULL:77947     NULL:0        
##  NULL:36061017500   NULL:175      NULL:CT   NULL:216905    NULL:192184   
##  NULL:36061018800   NULL:188      NULL:CT   NULL:148532    NULL:0        
##  NULL:36061018000   NULL:180      NULL:CT   NULL:212522    NULL:0        
##  NULL:36061021800   NULL:218      NULL:CT   NULL:181869    NULL:0        
##  NULL:36061017700   NULL:177      NULL:CT   NULL:174561    NULL:0        
##  NULL:36061004900   NULL:49       NULL:CT   NULL:207320    NULL:0        
##  NULL:36061010900   NULL:109      NULL:CT   NULL:175741    NULL:0        
##  NULL:36061015500   NULL:155      NULL:CT   NULL:197390    NULL:214654   
##  NULL:36061008603   NULL:86.03    NULL:CT   NULL:116670    NULL:153941   
##  NULL:36061023100   NULL:231      NULL:CT   NULL:182674    NULL:0        
##  NULL:36061011202   NULL:112.02   NULL:CT   NULL:78008     NULL:0        
##  NULL:36061012500   NULL:125      NULL:CT   NULL:174253    NULL:0        
##  NULL:36061006800   NULL:68       NULL:CT   NULL:174443    NULL:0        
##  NULL:36061019702   NULL:197.02   NULL:CT   NULL:47396     NULL:0        
##  NULL:36061030700   NULL:307      NULL:CT   NULL:84374     NULL:0        
##  NULL:36061002602   NULL:26.02    NULL:CT   NULL:103692    NULL:0        
##  NULL:36061002601   NULL:26.01    NULL:CT   NULL:106047    NULL:0        
##  NULL:36061011800   NULL:118      NULL:CT   NULL:176768    NULL:0        
##  NULL:36061015400   NULL:154      NULL:CT   NULL:177371    NULL:0        
##  NULL:36061009100   NULL:91       NULL:CT   NULL:179099    NULL:0        
##  NULL:36061021000   NULL:210      NULL:CT   NULL:203716    NULL:116287   
##  NULL:36061027900   NULL:279      NULL:CT   NULL:180013    NULL:0        
##     estimate     
##  Min.   :     0  
##  1st Qu.: 38994  
##  Median : 88253  
##  Mean   :111644  
##  3rd Qu.:179796  
##  Max.   :266885  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
##                  
## 

# Choropleth with col mapped to estimate
tm_shape(nyc_tracts_merge) +
  tm_fill(col="estimate")

tm_shape(nyc_tracts_merge) +
  tm_fill(col = "estimate") +
  # Add a water layer, tm_fill() with col = "grey90"
  # tm_shape(water) +
  # tm_fill(col = "grey90") +
  # Add a neighborhood layer, tm_borders()
  tm_shape(neighborhoods) +
  tm_borders()

# Find unique() nyc_tracts_merge$COUNTYFP
unique(nyc_tracts_merge$COUNTYFP)

## [1] "061"

# Add logical expression to pull out New York County
manhat_hoods <- neighborhoods[neighborhoods$CountyFIPS == "061", ]

tm_shape(nyc_tracts_merge) +
  tm_fill(col = "estimate") +
  # tm_shape(water) +
  # tm_fill(col = "grey90") +
  # Edit to use manhat_hoods instead
  tm_shape(manhat_hoods) +
  tm_borders() +
  # Add a tm_text() layer
  tm_text(text="NTAName")

# gsub() to replace " " with "\n"
manhat_hoods$name <- gsub(" ", "\n", manhat_hoods$NTAName)

# gsub() to replace "-" with "/\n"
manhat_hoods$name <- gsub("-", "/\n", manhat_hoods$name)

# Edit to map text to name, set size to 0.5
tm_shape(nyc_tracts_merge) +
    tm_fill(col = "estimate") +
    # tm_shape(water) +
    # tm_fill(col = "grey90") +
    tm_shape(manhat_hoods) +
    tm_borders() +
    tm_text(text = "name", size=0.5)

tm_shape(nyc_tracts_merge) +
  # Add title and change palette
  tm_fill(col = "estimate",
          title="Median Family Income\n(2016 Est.)",
          palette="Greens") +
  # Add tm_borders()
  tm_borders(col="grey60", lwd=0.5) +
  # tm_shape(water) +
  # tm_fill(col = "grey90") +
  tm_shape(manhat_hoods) +
  # Change col and lwd of neighborhood boundaries
  tm_borders(col="grey40", lwd=2) +
  tm_text(text = "name", size = 0.5) +
  # Add tm_credits()
  tm_credits("Source (by tract):\nhttps://www.ffiec.gov\nNew York County,\nNew York State", 
             position=c("right", "bottom"), size=0.6
             )

# Save map as "nyc_income_map.png"
# save_tmap(filename="nyc_income_map.png", width=4, height=7)